diff --git a/dev/news/index.html b/dev/news/index.html
index c2a5297..153101e 100644
--- a/dev/news/index.html
+++ b/dev/news/index.html
@@ -36,7 +36,8 @@ <h1>Changelog</h1>
 
     <div class="section level2">
 <h2 class="pkg-version" data-toc-text="development version" id="mlr3data-development-version">mlr3data (development version)<a class="anchor" aria-label="anchor" href="#mlr3data-development-version"></a></h2>
-</div>
+<ul><li>Readd <code>ames_housing</code> data set.</li>
+</ul></div>
     <div class="section level2">
 <h2 class="pkg-version" data-toc-text="0.8.0" id="mlr3data-080">mlr3data 0.8.0<a class="anchor" aria-label="anchor" href="#mlr3data-080"></a></h2><p class="text-muted">CRAN release: 2024-11-06</p>
 <ul><li>Move <code>ames_housing</code> to mlr3.</li>
diff --git a/dev/pkgdown.yml b/dev/pkgdown.yml
index d2e28b5..89806a6 100644
--- a/dev/pkgdown.yml
+++ b/dev/pkgdown.yml
@@ -2,7 +2,7 @@ pandoc: 3.1.11
 pkgdown: 2.1.1
 pkgdown_sha: ~
 articles: {}
-last_built: 2024-11-07T19:03Z
+last_built: 2024-11-07T20:37Z
 urls:
   reference: https://mlr3data.mlr-org.com/reference
   article: https://mlr3data.mlr-org.com/articles
diff --git a/dev/reference/ames_housing.html b/dev/reference/ames_housing.html
new file mode 100644
index 0000000..5c346a9
--- /dev/null
+++ b/dev/reference/ames_housing.html
@@ -0,0 +1,158 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>House Sales in Ames, Iowa — ames_housing • mlr3data</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.9/font.css" rel="stylesheet"><link href="../deps/JetBrains_Mono-0.4.9/font.css" rel="stylesheet"><link href="../deps/Roboto_Slab-0.4.9/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="House Sales in Ames, Iowa — ames_housing"><meta name="description" content='Regression task to predict house sale prices for Ames, Iowa.
+Contains 80 features and 2930 observations.
+Target column is "Sale_Price".'><meta property="og:description" content='Regression task to predict house sale prices for Ames, Iowa.
+Contains 80 features and 2930 observations.
+Target column is "Sale_Price".'><meta name="robots" content="noindex"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mlr3data</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">0.8.0.9000</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html"><span class="fa fa-file-alt"></span> Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://mlr3book.mlr-org.com"><span class="fa fa-link"></span> mlr3book</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlr-org/mlr3data/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://lmmisld-lmu-stats-slds.srv.mwn.de/mlr_invite/"><span class="fa fa-comments"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://stackoverflow.com/questions/tagged/mlr3"><span class="fa fab fa-stack-overflow"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://mlr-org.com/"><span class="fa fa-rss"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>House Sales in Ames, Iowa</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlr-org/mlr3data/blob/main/R/ames_housing.R" class="external-link"><code>R/ames_housing.R</code></a></small>
+      <div class="d-none name"><code>ames_housing.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Regression task to predict house sale prices for Ames, Iowa.</p>
+<p>Contains 80 features and 2930 observations.
+Target column is <code>"Sale_Price"</code>.</p>
+    </div>
+
+
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="st">"ames_housing"</span>, package <span class="op">=</span> <span class="st">"mlr3data"</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/str.html" class="external-link">str</a></span><span class="op">(</span><span class="va">ames_housing</span><span class="op">)</span></span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Classes ‘data.table’ and 'data.frame':	2930 obs. of  82 variables:</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Sale_Price        : int  215000 105000 172000 244000 189900 195500 213500 191500 236500 189000 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Alley             : Factor w/ 2 levels "Gravel","Paved": NA NA NA NA NA NA NA NA NA NA ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Bedroom_AbvGr     : int  3 2 3 3 3 3 2 2 2 3 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Bldg_Type         : Factor w/ 5 levels "Duplex","OneFam",..: 2 2 2 2 2 2 4 4 4 2 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Bsmt_Cond         : Factor w/ 5 levels "Excellent","Fair",..: 3 5 5 5 5 5 5 5 5 5 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Bsmt_Exposure     : Factor w/ 4 levels "Av","Gd","Mn",..: 2 4 4 4 4 4 3 4 4 4 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Bsmt_Full_Bath    : int  1 0 0 1 0 0 1 0 1 0 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Bsmt_Half_Bath    : int  0 0 0 0 0 0 0 0 0 0 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Bsmt_Qual         : Factor w/ 5 levels "Excellent","Fair",..: 5 5 5 5 3 5 3 3 3 5 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Bsmt_Unf_SF       : int  441 270 406 1045 137 324 722 1017 415 994 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ BsmtFin_SF_1      : int  639 468 923 1065 791 602 616 263 1180 0 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ BsmtFin_SF_2      : int  0 144 0 0 0 0 0 0 0 0 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ BsmtFin_Type_1    : Factor w/ 6 levels "ALQ","BLQ","GLQ",..: 2 5 1 1 3 3 3 1 3 6 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ BsmtFin_Type_2    : Factor w/ 6 levels "ALQ","BLQ","GLQ",..: 6 4 6 6 6 6 6 6 6 6 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Central_Air       : Factor w/ 2 levels "N","Y": 2 2 2 2 2 2 2 2 2 2 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Condition_1       : Factor w/ 9 levels "Artery","Feedr",..: 3 2 3 3 3 3 3 3 3 3 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Condition_2       : Factor w/ 8 levels "Artery","Feedr",..: 3 3 3 3 3 3 3 3 3 3 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Condition_3       : Factor w/ 8 levels "Artery","Feedr",..: 3 3 3 3 3 3 3 3 3 3 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Electrical        : Factor w/ 5 levels "FuseA","FuseF",..: 5 5 5 5 5 5 5 5 5 5 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Enclosed_Porch    : int  0 0 0 0 0 0 170 0 0 0 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Exter_Cond        : Factor w/ 5 levels "Excellent","Fair",..: 5 5 5 5 5 5 5 5 5 5 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Exter_Qual        : Factor w/ 4 levels "Excellent","Fair",..: 4 4 4 3 4 4 3 3 3 4 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Exterior_1st      : Factor w/ 16 levels "AsbShng","AsphShn",..: 4 14 15 4 14 14 6 7 6 14 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Exterior_2nd      : Factor w/ 17 levels "AsbShng","AsphShn",..: 11 15 16 4 15 15 6 7 6 15 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Fence             : Factor w/ 4 levels "Good_Privacy",..: NA 3 NA NA 3 NA NA NA NA NA ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Fireplace_Qu      : Factor w/ 5 levels "Excellent","Fair",..: 3 NA NA 5 5 3 NA NA 5 5 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Fireplaces        : int  2 0 0 2 1 1 0 0 1 1 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ First_Flr_SF      : int  1656 896 1329 2110 928 926 1338 1280 1616 1028 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Foundation        : Factor w/ 6 levels "BrkTil","CBlock",..: 2 2 2 2 3 3 3 3 3 3 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Full_Bath         : int  1 1 1 2 2 2 2 2 2 2 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Functional        : Factor w/ 8 levels "Maj1","Maj2",..: 8 8 8 8 8 8 8 8 8 8 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Garage_Area       : int  528 730 312 522 482 470 582 506 608 442 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Garage_Cars       : int  2 1 1 2 2 2 2 2 2 2 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Garage_Cond       : Factor w/ 5 levels "Excellent","Fair",..: 5 5 5 5 5 5 5 5 5 5 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Garage_Finish     : Factor w/ 3 levels "Fin","RFn","Unf": 1 3 3 1 1 1 1 2 2 1 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Garage_Qual       : Factor w/ 5 levels "Excellent","Fair",..: 5 5 5 5 5 5 5 5 5 5 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Garage_Type       : Factor w/ 6 levels "Attchd","Basment",..: 1 1 1 1 1 1 1 1 1 1 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Garage_Yr_Blt     : int  1960 1961 1958 1968 1997 1998 2001 1992 1995 1999 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Gr_Liv_Area       : int  1656 896 1329 2110 1629 1604 1338 1280 1616 1804 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Half_Bath         : int  0 0 1 1 1 1 0 0 0 1 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Heating           : Factor w/ 6 levels "Floor","GasA",..: 2 2 2 2 2 2 2 2 2 2 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Heating_QC        : Factor w/ 5 levels "Excellent","Fair",..: 2 5 5 1 3 1 1 1 1 3 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ House_Style       : Factor w/ 8 levels "One_Story","One_and_Half_Fin",..: 1 1 1 1 6 6 1 1 1 6 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Kitchen_AbvGr     : int  1 1 1 1 1 1 1 1 1 1 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Land_Contour      : Factor w/ 4 levels "Bnk","HLS","Low",..: 4 4 4 4 4 4 4 2 4 4 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Land_Slope        : Factor w/ 3 levels "Gtl","Mod","Sev": 1 1 1 1 1 1 1 1 1 1 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Lot_Area          : int  31770 11622 14267 11160 13830 9978 4920 5005 5389 7500 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Lot_Area_m2       : num  2952 1080 1325 1037 1285 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Lot_Config        : Factor w/ 5 levels "Corner","CulDSac",..: 1 5 1 1 5 5 5 5 5 5 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Lot_Frontage      : int  141 80 81 93 74 78 41 43 39 60 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Lot_Shape         : Factor w/ 4 levels "Irregular","Moderately_Irregular",..: 4 3 4 3 4 4 3 4 4 3 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Low_Qual_Fin_SF   : int  0 0 0 0 0 0 0 0 0 0 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Mas_Vnr_Area      : int  112 0 108 0 0 20 0 0 0 0 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Mas_Vnr_Type      : Factor w/ 5 levels "BrkCmn","BrkFace",..: 5 4 2 4 4 2 4 4 4 4 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Misc_Feature      : Factor w/ 5 levels "Elev","Gar2",..: NA NA 2 NA NA NA NA NA NA NA ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Misc_Feature_2    : Factor w/ 1 level "Othr": 1 1 1 1 1 1 1 1 1 1 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Misc_Val          : int  0 0 12500 0 0 0 0 0 0 0 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Mo_Sold           : int  5 6 6 4 3 6 4 1 3 6 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ MS_SubClass       : Factor w/ 16 levels "Duplex_All_Styles_and_Ages",..: 3 3 3 3 14 14 4 4 4 14 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ MS_Zoning         : Factor w/ 7 levels "A_agr","C_all",..: 6 5 6 6 6 6 6 6 6 6 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Neighborhood      : Factor w/ 28 levels "Bloomington_Heights",..: 16 16 16 16 9 9 26 26 26 9 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Open_Porch_SF     : int  62 0 36 0 34 36 0 82 152 60 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Overall_Cond      : Factor w/ 9 levels "Above_Average",..: 2 1 1 2 2 1 2 2 2 2 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Overall_Qual      : Factor w/ 10 levels "Above_Average",..: 1 2 1 6 2 1 9 9 9 6 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Paved_Drive       : Factor w/ 3 levels "Dirt_Gravel",..: 2 3 3 3 3 3 3 3 3 3 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Pool_Area         : int  0 0 0 0 0 0 0 0 0 0 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Pool_QC           : Factor w/ 4 levels "Excellent","Fair",..: NA NA NA NA NA NA NA NA NA NA ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Roof_Matl         : Factor w/ 8 levels "ClyTile","CompShg",..: 2 2 2 2 2 2 2 2 2 2 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Roof_Style        : Factor w/ 6 levels "Flat","Gable",..: 4 2 4 4 2 2 2 2 2 2 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Sale_Condition    : Factor w/ 6 levels "Abnorml","AdjLand",..: 5 5 5 5 5 5 5 5 5 5 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Sale_Type         : Factor w/ 10 levels "COD","CWD","Con",..: 10 10 10 10 10 10 10 10 10 10 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Screen_Porch      : int  0 120 0 0 0 0 0 144 0 0 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Second_Flr_SF     : int  0 0 0 0 701 678 0 0 0 776 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Street            : Factor w/ 2 levels "Grvl","Pave": 2 2 2 2 2 2 2 2 2 2 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Three_season_porch: int  0 0 0 0 0 0 0 0 0 0 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Total_Bsmt_SF     : int  1080 882 1329 2110 928 926 1338 1280 1595 994 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ TotRms_AbvGrd     : int  7 5 6 8 6 7 6 5 5 7 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Utilities         : Factor w/ 3 levels "AllPub","NoSeWa",..: 1 1 1 1 1 1 1 1 1 1 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Wood_Deck_SF      : int  210 140 393 0 212 360 0 0 237 140 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Year_Built        : int  1960 1961 1958 1968 1997 1998 2001 1992 1995 1999 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Year_Remod_Add    : int  1960 1961 1958 1968 1998 1998 2001 1992 1996 1999 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ Year_Sold         : int  2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  - attr(*, ".internal.selfref")=&lt;externalptr&gt; </span>
+</code></pre></div>
+    </div>
+  </main></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Marc Becker.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
+</div>
+
+    </footer></div>
+
+
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+
+
+  </body></html>
+
diff --git a/dev/reference/index.html b/dev/reference/index.html
index e884b10..85fa2f5 100644
--- a/dev/reference/index.html
+++ b/dev/reference/index.html
@@ -46,6 +46,12 @@ <h2 id="all-functions">All functions<a class="anchor" aria-label="anchor" href="
 
       <dl><dt>
 
+          <code><a href="ames_housing.html">ames_housing</a></code> <code><a href="ames_housing.html">mlr_tasks_ames_housing</a></code>
+
+        </dt>
+        <dd>House Sales in Ames, Iowa</dd>
+      </dl><dl><dt>
+
           <code><a href="bike_sharing.html">bike_sharing</a></code> <code><a href="bike_sharing.html">mlr_tasks_bike_sharing</a></code>
 
         </dt>
diff --git a/dev/reference/mlr_tasks_ames_housing.html b/dev/reference/mlr_tasks_ames_housing.html
new file mode 100644
index 0000000..133e098
--- /dev/null
+++ b/dev/reference/mlr_tasks_ames_housing.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://mlr3data.mlr-org.com/dev/reference/ames_housing.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://mlr3data.mlr-org.com/dev/reference/ames_housing.html">
+  </head>
+</html>
+
diff --git a/dev/search.json b/dev/search.json
index 240a4c0..938efc1 100644
--- a/dev/search.json
+++ b/dev/search.json
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-[{"path":"https://mlr3data.mlr-org.com/dev/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Michel Lang. Contributor. Marc Becker. Maintainer, author.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Becker M (2024). mlr3data: Collection Machine Learning Data Sets 'mlr3'. R package version 0.8.0.9000, https://github.com/mlr-org/mlr3data.","code":"@Manual{,   title = {mlr3data: Collection of Machine Learning Data Sets for 'mlr3'},   author = {Marc Becker},   year = {2024},   note = {R package version 0.8.0.9000},   url = {https://github.com/mlr-org/mlr3data}, }"},{"path":"https://mlr3data.mlr-org.com/dev/index.html","id":"mlr3data","dir":"","previous_headings":"","what":"Collection of Machine Learning Data Sets for mlr3","title":"Collection of Machine Learning Data Sets for mlr3","text":"small collection interesting educational machine learning data sets used examples mlr3 book, mlr3 gallery, examples mlr3 packages. data sets properly preprocessed ready analyzed machine learning algorithms. Currently contains following data sets: Housing prices Kings County [link] Titanic passenger survival data [link] Optical recognition handwritten digits data [link] Major League Baseball statistics 1962-2012 [link] Indian Liver Patient Data [link] Bike Sharing Demand [link]","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/bike_sharing.html","id":null,"dir":"Reference","previous_headings":"","what":"Bike Sharing Demand — bike_sharing","title":"Bike Sharing Demand — bike_sharing","text":"Regression data predict total count bikes rented. Contains 13 features 17379 observations. Target column \"count\".","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/bike_sharing.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Bike Sharing Demand — bike_sharing","text":"https://archive.ics.uci.edu/ml/datasets/bike+sharing+dataset","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/bike_sharing.html","id":"pre-processing","dir":"Reference","previous_headings":"","what":"Pre-processing","title":"Bike Sharing Demand — bike_sharing","text":"columns renamed. instant, \"registered\" \"casual\" column removed. \"season\" \"weather\" converted factor(). \"holiday\" \"working_day\" converted logical().","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/bike_sharing.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bike Sharing Demand — bike_sharing","text":"","code":"data(\"bike_sharing\", package = \"mlr3data\") str(bike_sharing) #> Classes ‘data.table’ and 'data.frame':\t17379 obs. of  14 variables: #>  $ date                : chr  \"2011-01-01\" \"2011-01-01\" \"2011-01-01\" \"2011-01-01\" ... #>  $ season              : Factor w/ 4 levels \"winter\",\"spring\",..: 1 1 1 1 1 1 1 1 1 1 ... #>  $ year                : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ month               : int  1 1 1 1 1 1 1 1 1 1 ... #>  $ hour                : int  0 1 2 3 4 5 6 7 8 9 ... #>  $ holiday             : logi  FALSE FALSE FALSE FALSE FALSE FALSE ... #>  $ weekday             : int  6 6 6 6 6 6 6 6 6 6 ... #>  $ working_day         : logi  FALSE FALSE FALSE FALSE FALSE FALSE ... #>  $ weather             : Factor w/ 4 levels \"1\",\"2\",\"3\",\"4\": 1 1 1 1 1 2 1 1 1 1 ... #>  $ temperature         : num  0.24 0.22 0.22 0.24 0.24 0.24 0.22 0.2 0.24 0.32 ... #>  $ apparent_temperature: num  0.288 0.273 0.273 0.288 0.288 ... #>  $ humidity            : num  0.81 0.8 0.8 0.75 0.75 0.75 0.8 0.86 0.75 0.76 ... #>  $ windspeed           : num  0 0 0 0 0 0.0896 0 0 0 0 ... #>  $ count               : int  16 40 32 13 1 1 2 3 8 14 ... #>  - attr(*, \".internal.selfref\")=<externalptr>"},{"path":"https://mlr3data.mlr-org.com/dev/reference/energy_usage.html","id":null,"dir":"Reference","previous_headings":"","what":"Power Consumption of Kitchen Appliances in Ames, Iowa — energy_usage","title":"Power Consumption of Kitchen Appliances in Ames, Iowa — energy_usage","text":"Data power consumption kitchen appliances Ames, Iowa. Extends ames_housing data set. Contains 720 features 2930 observations.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/energy_usage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Power Consumption of Kitchen Appliances in Ames, Iowa — energy_usage","text":"","code":"data(\"energy_usage\", package = \"mlr3data\") str(energy_usage) #> Classes ‘data.table’ and 'data.frame':\t2930 obs. of  720 variables: #>  $ att1  : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att2  : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att3  : num  0.715 0.267 0.48 0.599 0.249 ... #>  $ att4  : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att5  : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att6  : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att7  : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att8  : num  0.715 0.267 0.464 0.503 0.249 ... #>  $ att9  : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att10 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att11 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att12 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att13 : num  0.715 0.267 0.464 0.599 0.249 ... #>  $ att14 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att15 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att16 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att17 : num  0.715 0.267 0.633 0.599 0.345 ... #>  $ att18 : num  0.715 0.267 0.48 0.599 0.249 ... #>  $ att19 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att20 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att21 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att22 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att23 : num  0.715 0.267 0.464 0.599 0.249 ... #>  $ att24 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att25 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att26 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att27 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att28 : num  0.715 0.267 0.464 0.503 0.249 ... #>  $ att29 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att30 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att31 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att32 : num  0.715 0.267 0.48 0.503 0.345 ... #>  $ att33 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att34 : num  0.715 0.267 0.464 0.599 0.249 ... #>  $ att35 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att36 : num  0.715 0.267 0.48 0.503 0.345 ... #>  $ att37 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att38 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att39 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att40 : num  0.715 0.267 0.464 0.503 0.249 ... #>  $ att41 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att42 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att43 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att44 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att45 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att46 : num  0.715 0.267 0.633 0.599 0.345 ... #>  $ att47 : num  0.715 0.267 0.48 0.599 0.249 ... #>  $ att48 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att49 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att50 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att51 : num  0.715 0.267 0.464 0.599 0.249 ... #>  $ att52 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att53 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att54 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att55 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att56 : num  0.715 0.267 0.464 0.599 0.249 ... #>  $ att57 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att58 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att59 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att60 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att61 : num  0.715 0.267 0.464 0.599 0.249 ... #>  $ att62 : num  0.715 0.267 0.48 0.503 0.345 ... #>  $ att63 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att64 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att65 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att66 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att67 : num  0.715 0.267 0.464 0.599 0.249 ... #>  $ att68 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att69 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att70 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att71 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att72 : num  0.715 0.267 0.464 0.599 0.249 ... #>  $ att73 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att74 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att75 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att76 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att77 : num  0.715 0.267 0.48 0.599 0.249 ... #>  $ att78 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att79 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att80 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att81 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att82 : num  0.715 0.267 0.464 0.599 0.249 ... #>  $ att83 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att84 : num  0.715 0.267 0.48 0.503 0.345 ... #>  $ att85 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att86 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att87 : num  0.715 0.267 0.633 0.599 0.249 ... #>  $ att88 : num  0.715 0.267 0.48 0.503 0.345 ... #>  $ att89 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att90 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att91 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att92 : num  0.715 0.267 0.48 0.503 0.249 ... #>  $ att93 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att94 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att95 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att96 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att97 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att98 : num  0.715 0.267 0.464 0.599 0.249 ... #>  $ att99 : num  0.715 0.267 0.48 0.599 0.345 ... #>   [list output truncated] #>  - attr(*, \".internal.selfref\")=<externalptr>"},{"path":"https://mlr3data.mlr-org.com/dev/reference/ilpd.html","id":null,"dir":"Reference","previous_headings":"","what":"Indian Liver Patient Dataset — ilpd","title":"Indian Liver Patient Dataset — ilpd","text":"Classification data predict whether person liver patient. Obtained using mlr3oml package. Contains 538 observations 10 features. Target column \"diseased\".","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/ilpd.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Indian Liver Patient Dataset — ilpd","text":"https://www.openml.org/d/1480","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/ilpd.html","id":"pre-processing","dir":"Reference","previous_headings":"","what":"Pre-processing","title":"Indian Liver Patient Dataset — ilpd","text":"variables renamed. target variable re-encoded \"yes\" \"\".","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/ilpd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Indian Liver Patient Dataset — ilpd","text":"","code":"data(\"ilpd\", package = \"mlr3data\") str(ilpd) #> 'data.frame':\t583 obs. of  11 variables: #>  $ age                   : int  65 62 62 58 72 46 26 29 17 55 ... #>  $ gender                : Factor w/ 2 levels \"Female\",\"Male\": 1 2 2 2 2 2 1 1 2 2 ... #>  $ total_bilirubin       : num  0.7 10.9 7.3 1 3.9 1.8 0.9 0.9 0.9 0.7 ... #>  $ direct_bilirubin      : num  0.1 5.5 4.1 0.4 2 0.7 0.2 0.3 0.3 0.2 ... #>  $ alkaline_phosphatase  : int  187 699 490 182 195 208 154 202 202 290 ... #>  $ alanine_transaminase  : int  16 64 60 14 27 19 16 14 22 53 ... #>  $ aspartate_transaminase: int  18 100 68 20 59 14 12 11 19 58 ... #>  $ total_protein         : num  6.8 7.5 7 6.8 7.3 7.6 7 6.7 7.4 6.8 ... #>  $ albumin               : num  3.3 3.2 3.3 3.4 2.4 4.4 3.5 3.6 4.1 3.4 ... #>  $ albumin_globulin_ratio: num  0.9 0.74 0.89 1 0.4 1.3 1 1.1 1.2 1 ... #>  $ diseased              : Factor w/ 2 levels \"yes\",\"no\": 1 1 1 1 1 1 1 1 2 1 ..."},{"path":"https://mlr3data.mlr-org.com/dev/reference/kc_housing.html","id":null,"dir":"Reference","previous_headings":"","what":"House Sales in King County — kc_housing","title":"House Sales in King County — kc_housing","text":"Regression task predict house sale prices King County, including Seattle, May 2014 May 2015. Contains 19 features 21613 observations. Target column \"price\".","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/kc_housing.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"House Sales in King County — kc_housing","text":"https://www.kaggle.com/datasets/harlfoxem/housesalesprediction","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/kc_housing.html","id":"pre-processing","dir":"Reference","previous_headings":"","what":"Pre-processing","title":"House Sales in King County — kc_housing","text":"Id column removed. Dates column \"date\" converted strings POSIXct. Values 0 feature \"yr_renovated\" replaced NA. Values 0 feature \"sqft_basement\" replaced NA. Feature \"waterfront\" converted logical.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/kc_housing.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"House Sales in King County — kc_housing","text":"","code":"data(\"kc_housing\", package = \"mlr3data\") str(kc_housing) #> 'data.frame':\t21613 obs. of  20 variables: #>  $ date         : POSIXct, format: \"2014-10-13\" \"2014-12-09\" ... #>  $ price        : num  221900 538000 180000 604000 510000 ... #>  $ bedrooms     : int  3 3 2 4 3 4 3 3 3 3 ... #>  $ bathrooms    : num  1 2.25 1 3 2 4.5 2.25 1.5 1 2.5 ... #>  $ sqft_living  : int  1180 2570 770 1960 1680 5420 1715 1060 1780 1890 ... #>  $ sqft_lot     : int  5650 7242 10000 5000 8080 101930 6819 9711 7470 6560 ... #>  $ floors       : num  1 2 1 1 1 1 2 1 1 2 ... #>  $ waterfront   : logi  FALSE FALSE FALSE FALSE FALSE FALSE ... #>  $ view         : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ condition    : int  3 3 3 5 3 3 3 3 3 3 ... #>  $ grade        : int  7 7 6 7 8 11 7 7 7 7 ... #>  $ sqft_above   : int  1180 2170 770 1050 1680 3890 1715 1060 1050 1890 ... #>  $ sqft_basement: int  NA 400 NA 910 NA 1530 NA NA 730 NA ... #>  $ yr_built     : int  1955 1951 1933 1965 1987 2001 1995 1963 1960 2003 ... #>  $ yr_renovated : int  NA 1991 NA NA NA NA NA NA NA NA ... #>  $ zipcode      : int  98178 98125 98028 98136 98074 98053 98003 98198 98146 98038 ... #>  $ lat          : num  47.5 47.7 47.7 47.5 47.6 ... #>  $ long         : num  -122 -122 -122 -122 -122 ... #>  $ sqft_living15: int  1340 1690 2720 1360 1800 4760 2238 1650 1780 2390 ... #>  $ sqft_lot15   : int  5650 7639 8062 5000 7503 101930 6819 9711 8113 7570 ... #>  - attr(*, \"index\")= int(0)"},{"path":"https://mlr3data.mlr-org.com/dev/reference/mlr3data-package.html","id":null,"dir":"Reference","previous_headings":"","what":"mlr3data: Collection of Machine Learning Data Sets for 'mlr3' — mlr3data-package","title":"mlr3data: Collection of Machine Learning Data Sets for 'mlr3' — mlr3data-package","text":"small collection interesting educational machine learning data sets used examples 'mlr3' book (https://mlr3book.mlr-org.com), use case gallery (https://mlr3gallery.mlr-org.com), examples. data sets properly preprocessed ready analyzed machine learning algorithms. Data sets automatically added dictionary tasks 'mlr3' loaded.","code":""},{"path":[]},{"path":"https://mlr3data.mlr-org.com/dev/reference/mlr3data-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"mlr3data: Collection of Machine Learning Data Sets for 'mlr3' — mlr3data-package","text":"Maintainer: Marc Becker marcbecker@posteo.de (ORCID) contributors: Michel Lang michellang@gmail.com (ORCID) [contributor]","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/moneyball.html","id":null,"dir":"Reference","previous_headings":"","what":"Major League Baseball Statistics 1962-2012 — moneyball","title":"Major League Baseball Statistics 1962-2012 — moneyball","text":"Regression data predict number runs scored. Obtained using mlr3oml package. Contains 14 features 1232 observations. Target column \"rs\".","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/moneyball.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Major League Baseball Statistics 1962-2012 — moneyball","text":"https://www.openml.org/d/41021","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/moneyball.html","id":"pre-processing","dir":"Reference","previous_headings":"","what":"Pre-processing","title":"Major League Baseball Statistics 1962-2012 — moneyball","text":"variable names converted upper case lower case. variables \"year\", \"rs\", \"ra\", \"w\"` coerced integers.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/moneyball.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Major League Baseball Statistics 1962-2012 — moneyball","text":"","code":"data(\"moneyball\", package = \"mlr3data\") str(moneyball) #> 'data.frame':\t1232 obs. of  15 variables: #>  $ team        : Factor w/ 39 levels \"ARI\",\"ATL\",\"BAL\",..: 1 2 3 4 5 6 7 8 9 10 ... #>  $ league      : Factor w/ 2 levels \"AL\",\"NL\": 2 2 1 1 2 1 2 1 2 1 ... #>  $ year        : int  2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 ... #>  $ rs          : int  734 700 712 734 613 748 669 667 758 726 ... #>  $ ra          : int  688 600 705 806 759 676 588 845 890 670 ... #>  $ w           : int  81 94 93 69 61 85 97 68 64 88 ... #>  $ obp         : num  0.328 0.32 0.311 0.315 0.302 0.318 0.315 0.324 0.33 0.335 ... #>  $ slg         : num  0.418 0.389 0.417 0.415 0.378 0.422 0.411 0.381 0.436 0.422 ... #>  $ ba          : num  0.259 0.247 0.247 0.26 0.24 0.255 0.251 0.251 0.274 0.268 ... #>  $ playoffs    : Factor w/ 2 levels \"0\",\"1\": 1 2 2 1 1 1 2 1 1 2 ... #>  $ rankseason  : Factor w/ 8 levels \"1\",\"2\",\"3\",\"4\",..: NA 4 5 NA NA NA 2 NA NA 6 ... #>  $ rankplayoffs: Factor w/ 5 levels \"1\",\"2\",\"3\",\"4\",..: NA 5 4 NA NA NA 4 NA NA 2 ... #>  $ g           : Factor w/ 8 levels \"158\",\"159\",\"160\",..: 5 5 5 5 5 5 5 5 5 5 ... #>  $ oobp        : num  0.317 0.306 0.315 0.331 0.335 0.319 0.305 0.336 0.357 0.314 ... #>  $ oslg        : num  0.415 0.378 0.403 0.428 0.424 0.405 0.39 0.43 0.47 0.402 ..."},{"path":"https://mlr3data.mlr-org.com/dev/reference/optdigits.html","id":null,"dir":"Reference","previous_headings":"","what":"Optical Recognition of Handwritten Digits — optdigits","title":"Optical Recognition of Handwritten Digits — optdigits","text":"Classification data predict handwritten digits. Obtained using mlr3oml package. Binarized version original data set. multi-class target column converted two-class nominal target column re-labeling majority class positive (\"P\") others negative (\"N\"). Originally converted Quan Sun. Contains 64 features 5620 observations. Target column \"binaryclass\".","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/optdigits.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Optical Recognition of Handwritten Digits — optdigits","text":"https://www.openml.org/d/980","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/optdigits.html","id":"pre-processing","dir":"Reference","previous_headings":"","what":"Pre-processing","title":"Optical Recognition of Handwritten Digits — optdigits","text":"feature variables \"input1\", ..., \"input64\" (number pixels block) coerced integers. target variable renamed \"binaryClass\" \"binaryclass\".","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/optdigits.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Optical Recognition of Handwritten Digits — optdigits","text":"","code":"data(\"optdigits\", package = \"mlr3data\") str(optdigits) #> 'data.frame':\t5620 obs. of  65 variables: #>  $ input1     : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input2     : int  1 0 0 0 0 0 0 0 0 0 ... #>  $ input3     : int  6 10 8 0 5 11 1 8 15 3 ... #>  $ input4     : int  15 16 15 3 14 16 11 10 2 13 ... #>  $ input5     : int  12 6 16 11 4 10 13 8 14 13 ... #>  $ input6     : int  1 0 13 16 0 1 11 7 13 2 ... #>  $ input7     : int  0 0 0 0 0 0 7 2 2 0 ... #>  $ input8     : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input9     : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input10    : int  7 7 1 0 0 4 0 1 0 6 ... #>  $ input11    : int  16 16 11 5 13 16 9 15 16 16 ... #>  $ input12    : int  6 8 9 16 8 10 14 14 15 12 ... #>  $ input13    : int  6 16 11 11 0 15 6 12 12 10 ... #>  $ input14    : int  10 5 16 13 0 8 4 12 13 8 ... #>  $ input15    : int  0 0 1 7 0 0 3 4 8 0 ... #>  $ input16    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input17    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input18    : int  8 11 0 3 3 4 0 7 2 9 ... #>  $ input19    : int  16 16 0 15 14 16 16 15 16 15 ... #>  $ input20    : int  2 0 0 8 4 3 12 12 12 12 ... #>  $ input21    : int  0 6 7 1 0 11 16 5 1 16 ... #>  $ input22    : int  11 14 14 15 0 13 15 0 6 6 ... #>  $ input23    : int  2 3 0 6 0 0 2 0 10 0 ... #>  $ input24    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input25    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input26    : int  5 12 0 11 6 1 5 5 7 10 ... #>  $ input27    : int  16 12 3 16 16 14 16 14 15 16 ... #>  $ input28    : int  3 0 4 16 14 6 10 12 3 16 ... #>  $ input29    : int  0 0 14 16 9 9 4 15 0 13 ... #>  $ input30    : int  5 11 12 16 2 14 12 7 5 0 ... #>  $ input31    : int  7 11 2 10 0 0 6 0 8 0 ... #>  $ input32    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input33    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input34    : int  7 12 1 1 4 0 1 0 5 1 ... #>  $ input35    : int  13 12 16 4 16 0 1 0 12 12 ... #>  $ input36    : int  3 0 16 4 3 0 0 0 0 16 ... #>  $ input37    : int  0 0 16 13 4 12 0 2 0 12 ... #>  $ input38    : int  8 8 16 10 11 10 10 13 8 14 ... #>  $ input39    : int  7 12 10 2 2 0 4 0 8 4 ... #>  $ input40    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input41    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input42    : int  4 7 2 0 0 0 0 0 5 0 ... #>  $ input43    : int  12 15 12 0 14 0 0 0 12 11 ... #>  $ input44    : int  0 1 16 0 3 6 0 0 0 8 ... #>  $ input45    : int  1 0 10 15 0 16 5 4 7 0 ... #>  $ input46    : int  13 13 0 4 4 6 10 12 15 3 ... #>  $ input47    : int  5 11 0 0 11 0 0 0 5 12 ... #>  $ input48    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input49    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input50    : int  0 0 0 0 0 0 0 0 5 0 ... #>  $ input51    : int  14 16 2 0 10 5 0 6 16 13 ... #>  $ input52    : int  9 8 16 3 8 15 8 7 13 11 ... #>  $ input53    : int  15 10 4 16 4 15 15 14 16 8 ... #>  $ input54    : int  9 15 0 0 11 8 3 5 6 13 ... #>  $ input55    : int  0 3 0 0 12 8 0 0 0 12 ... #>  $ input56    : int  0 0 0 0 0 3 0 0 0 0 ... #>  $ input57    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input58    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input59    : int  6 10 9 0 4 10 1 4 10 3 ... #>  $ input60    : int  14 16 14 1 12 16 13 13 12 15 ... #>  $ input61    : int  7 15 0 15 14 16 5 8 5 11 ... #>  $ input62    : int  1 3 0 2 7 16 0 0 0 6 ... #>  $ input63    : int  0 0 0 0 0 16 0 0 0 0 ... #>  $ input64    : int  0 0 0 0 0 6 0 0 0 0 ... #>  $ binaryclass: Factor w/ 2 levels \"P\",\"N\": 2 2 2 2 2 2 2 2 2 2 ..."},{"path":"https://mlr3data.mlr-org.com/dev/reference/penguins_simple.html","id":null,"dir":"Reference","previous_headings":"","what":"Simplified Palmer Penguins Data Set — penguins_simple","title":"Simplified Palmer Penguins Data Set — penguins_simple","text":"Classification data predict species penguins palmerpenguins package. better alternative iris data set.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/penguins_simple.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Simplified Palmer Penguins Data Set — penguins_simple","text":"palmerpenguins","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/penguins_simple.html","id":"pre-processing","dir":"Reference","previous_headings":"","what":"Pre-processing","title":"Simplified Palmer Penguins Data Set — penguins_simple","text":"unit measurement removed column names. Lengths given millimeters (mm), weight gram (g). Observations missing values removed. Factor variables one-hot encoded.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/penguins_simple.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Simplified Palmer Penguins Data Set — penguins_simple","text":"Gorman KB, Williams TD, Fraser WR (2014). “Ecological Sexual Dimorphism Environmental Variability within Community Antarctic Penguins (Genus Pygoscelis).” PLoS ONE, 9(3), e90081. doi:10.1371/journal.pone.0090081 . https://github.com/allisonhorst/palmerpenguins","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/penguins_simple.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simplified Palmer Penguins Data Set — penguins_simple","text":"","code":"data(\"penguins_simple\", package = \"mlr3data\") str(penguins_simple) #> 'data.frame':\t333 obs. of  11 variables: #>  $ species         : Factor w/ 3 levels \"Adelie\",\"Chinstrap\",..: 1 1 1 1 1 1 1 1 1 1 ... #>  $ bill_depth      : num  18.7 17.4 18 19.3 20.6 17.8 19.6 17.6 21.2 21.1 ... #>  $ bill_length     : num  39.1 39.5 40.3 36.7 39.3 38.9 39.2 41.1 38.6 34.6 ... #>  $ body_mass       : int  3750 3800 3250 3450 3650 3625 4675 3200 3800 4400 ... #>  $ flipper_length  : int  181 186 195 193 190 181 195 182 191 198 ... #>  $ year            : int  2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 ... #>  $ island.Biscoe   : num  0 0 0 0 0 0 0 0 0 0 ... #>  $ island.Dream    : num  0 0 0 0 0 0 0 0 0 0 ... #>  $ island.Torgersen: num  1 1 1 1 1 1 1 1 1 1 ... #>  $ sex.female      : num  0 1 1 1 0 1 0 1 0 0 ... #>  $ sex.male        : num  1 0 0 0 1 0 1 0 1 1 ..."},{"path":"https://mlr3data.mlr-org.com/dev/reference/titanic.html","id":null,"dir":"Reference","previous_headings":"","what":"Titanic — titanic","title":"Titanic — titanic","text":"Classification data predict fate passengers ocean liner \"Titanic\". Contains 10 features 1309 observations. Target column \"Survived\".","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/titanic.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Titanic — titanic","text":"titanic https://www.kaggle.com/c/titanic/data","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/titanic.html","id":"pre-processing","dir":"Reference","previous_headings":"","what":"Pre-processing","title":"Titanic — titanic","text":"column names changed snake_case. training test set joined. Observations test set missing value target column \"survived\". Column '\"survived\"' re-encoded factor levels '\"yes\"' '\"\"'. Id column removed. Passenger class \"pclass\" converted ordered factor. Features \"sex\" \"embarked\" converted factors. Empty strings \"cabin\" \"embarked\" encoded missing values.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/titanic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Titanic — titanic","text":"","code":"data(\"titanic\", package = \"mlr3data\") str(titanic) #> 'data.frame':\t1309 obs. of  11 variables: #>  $ survived: Factor w/ 2 levels \"yes\",\"no\": 2 1 1 1 2 2 2 2 1 1 ... #>  $ pclass  : Ord.factor w/ 3 levels \"1\"<\"2\"<\"3\": 3 1 3 1 3 3 1 3 3 2 ... #>  $ name    : chr  \"Braund, Mr. Owen Harris\" \"Cumings, Mrs. John Bradley (Florence Briggs Thayer)\" \"Heikkinen, Miss. Laina\" \"Futrelle, Mrs. Jacques Heath (Lily May Peel)\" ... #>  $ sex     : Factor w/ 2 levels \"female\",\"male\": 2 1 1 1 2 2 2 2 1 1 ... #>  $ age     : num  22 38 26 35 35 NA 54 2 27 14 ... #>  $ sib_sp  : int  1 1 0 1 0 0 0 3 0 1 ... #>  $ parch   : int  0 0 0 0 0 0 0 1 2 0 ... #>  $ ticket  : chr  \"A/5 21171\" \"PC 17599\" \"STON/O2. 3101282\" \"113803\" ... #>  $ fare    : num  7.25 71.28 7.92 53.1 8.05 ... #>  $ cabin   : chr  NA \"C85\" NA \"C123\" ... #>  $ embarked: Factor w/ 3 levels \"C\",\"Q\",\"S\": 3 1 3 3 3 2 3 3 3 1 ..."},{"path":[]},{"path":"https://mlr3data.mlr-org.com/dev/news/index.html","id":"mlr3data-080","dir":"Changelog","previous_headings":"","what":"mlr3data 0.8.0","title":"mlr3data 0.8.0","text":"CRAN release: 2024-11-06 Move ames_housing mlr3.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/news/index.html","id":"mlr3data-070","dir":"Changelog","previous_headings":"","what":"mlr3data 0.7.0","title":"mlr3data 0.7.0","text":"CRAN release: 2023-06-29 Added dataset ames_housing, used book mlr3. Added dataset energy_usage, used book mlr3.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/news/index.html","id":"mlr3data-061","dir":"Changelog","previous_headings":"","what":"mlr3data 0.6.1","title":"mlr3data 0.6.1","text":"CRAN release: 2022-08-15 Fixed documentation CRAN notes. Added simplified version penguins data set penguins_simple. Added labels data sets.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/news/index.html","id":"mlr3data-060","dir":"Changelog","previous_headings":"","what":"mlr3data 0.6.0","title":"mlr3data 0.6.0","text":"CRAN release: 2022-03-18 Added simplified version penguins data set penguins_simple. Added labels data sets.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/news/index.html","id":"mlr3data-050","dir":"Changelog","previous_headings":"","what":"mlr3data 0.5.0","title":"mlr3data 0.5.0","text":"CRAN release: 2021-06-29 Added bike sharing regression task UCI. mlr3 tasks now loaded lazily.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/news/index.html","id":"mlr3data-040","dir":"Changelog","previous_headings":"","what":"mlr3data 0.4.0","title":"mlr3data 0.4.0","text":"CRAN release: 2021-06-08 Added Indian liver patient dataset ilpd.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/news/index.html","id":"mlr3data-031","dir":"Changelog","previous_headings":"","what":"mlr3data 0.3.1","title":"mlr3data 0.3.1","text":"CRAN release: 2021-03-19 Removed penguins data set; now included mlr3.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/news/index.html","id":"mlr3data-030","dir":"Changelog","previous_headings":"","what":"mlr3data 0.3.0","title":"mlr3data 0.3.0","text":"CRAN release: 2020-12-04 Added optdigits data obtained via OpenML package. Added moneyball data obtained via OpenML package.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/news/index.html","id":"mlr3data-021","dir":"Changelog","previous_headings":"","what":"mlr3data 0.2.1","title":"mlr3data 0.2.1","text":"CRAN release: 2020-10-05 Removed dependency orphaned package bibtex.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/news/index.html","id":"mlr3data-020","dir":"Changelog","previous_headings":"","what":"mlr3data 0.2.0","title":"mlr3data 0.2.0","text":"CRAN release: 2020-08-03 Data sets now automatically added task dictionary mlr3 loaded. Added penguins data palmerpenguins package.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/news/index.html","id":"mlr3data-010","dir":"Changelog","previous_headings":"","what":"mlr3data 0.1.0","title":"mlr3data 0.1.0","text":"CRAN release: 2020-02-10 Initial release.","code":""}]
+[{"path":"https://mlr3data.mlr-org.com/dev/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Michel Lang. Contributor. Marc Becker. Maintainer, author.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Becker M (2024). mlr3data: Collection Machine Learning Data Sets 'mlr3'. R package version 0.8.0.9000, https://github.com/mlr-org/mlr3data.","code":"@Manual{,   title = {mlr3data: Collection of Machine Learning Data Sets for 'mlr3'},   author = {Marc Becker},   year = {2024},   note = {R package version 0.8.0.9000},   url = {https://github.com/mlr-org/mlr3data}, }"},{"path":"https://mlr3data.mlr-org.com/dev/index.html","id":"mlr3data","dir":"","previous_headings":"","what":"Collection of Machine Learning Data Sets for mlr3","title":"Collection of Machine Learning Data Sets for mlr3","text":"small collection interesting educational machine learning data sets used examples mlr3 book, mlr3 gallery, examples mlr3 packages. data sets properly preprocessed ready analyzed machine learning algorithms. Currently contains following data sets: Housing prices Kings County [link] Titanic passenger survival data [link] Optical recognition handwritten digits data [link] Major League Baseball statistics 1962-2012 [link] Indian Liver Patient Data [link] Bike Sharing Demand [link]","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/ames_housing.html","id":null,"dir":"Reference","previous_headings":"","what":"House Sales in Ames, Iowa — ames_housing","title":"House Sales in Ames, Iowa — ames_housing","text":"Regression task predict house sale prices Ames, Iowa. Contains 80 features 2930 observations. Target column \"Sale_Price\".","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/ames_housing.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"House Sales in Ames, Iowa — ames_housing","text":"","code":"data(\"ames_housing\", package = \"mlr3data\") str(ames_housing) #> Classes ‘data.table’ and 'data.frame':\t2930 obs. of  82 variables: #>  $ Sale_Price        : int  215000 105000 172000 244000 189900 195500 213500 191500 236500 189000 ... #>  $ Alley             : Factor w/ 2 levels \"Gravel\",\"Paved\": NA NA NA NA NA NA NA NA NA NA ... #>  $ Bedroom_AbvGr     : int  3 2 3 3 3 3 2 2 2 3 ... #>  $ Bldg_Type         : Factor w/ 5 levels \"Duplex\",\"OneFam\",..: 2 2 2 2 2 2 4 4 4 2 ... #>  $ Bsmt_Cond         : Factor w/ 5 levels \"Excellent\",\"Fair\",..: 3 5 5 5 5 5 5 5 5 5 ... #>  $ Bsmt_Exposure     : Factor w/ 4 levels \"Av\",\"Gd\",\"Mn\",..: 2 4 4 4 4 4 3 4 4 4 ... #>  $ Bsmt_Full_Bath    : int  1 0 0 1 0 0 1 0 1 0 ... #>  $ Bsmt_Half_Bath    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ Bsmt_Qual         : Factor w/ 5 levels \"Excellent\",\"Fair\",..: 5 5 5 5 3 5 3 3 3 5 ... #>  $ Bsmt_Unf_SF       : int  441 270 406 1045 137 324 722 1017 415 994 ... #>  $ BsmtFin_SF_1      : int  639 468 923 1065 791 602 616 263 1180 0 ... #>  $ BsmtFin_SF_2      : int  0 144 0 0 0 0 0 0 0 0 ... #>  $ BsmtFin_Type_1    : Factor w/ 6 levels \"ALQ\",\"BLQ\",\"GLQ\",..: 2 5 1 1 3 3 3 1 3 6 ... #>  $ BsmtFin_Type_2    : Factor w/ 6 levels \"ALQ\",\"BLQ\",\"GLQ\",..: 6 4 6 6 6 6 6 6 6 6 ... #>  $ Central_Air       : Factor w/ 2 levels \"N\",\"Y\": 2 2 2 2 2 2 2 2 2 2 ... #>  $ Condition_1       : Factor w/ 9 levels \"Artery\",\"Feedr\",..: 3 2 3 3 3 3 3 3 3 3 ... #>  $ Condition_2       : Factor w/ 8 levels \"Artery\",\"Feedr\",..: 3 3 3 3 3 3 3 3 3 3 ... #>  $ Condition_3       : Factor w/ 8 levels \"Artery\",\"Feedr\",..: 3 3 3 3 3 3 3 3 3 3 ... #>  $ Electrical        : Factor w/ 5 levels \"FuseA\",\"FuseF\",..: 5 5 5 5 5 5 5 5 5 5 ... #>  $ Enclosed_Porch    : int  0 0 0 0 0 0 170 0 0 0 ... #>  $ Exter_Cond        : Factor w/ 5 levels \"Excellent\",\"Fair\",..: 5 5 5 5 5 5 5 5 5 5 ... #>  $ Exter_Qual        : Factor w/ 4 levels \"Excellent\",\"Fair\",..: 4 4 4 3 4 4 3 3 3 4 ... #>  $ Exterior_1st      : Factor w/ 16 levels \"AsbShng\",\"AsphShn\",..: 4 14 15 4 14 14 6 7 6 14 ... #>  $ Exterior_2nd      : Factor w/ 17 levels \"AsbShng\",\"AsphShn\",..: 11 15 16 4 15 15 6 7 6 15 ... #>  $ Fence             : Factor w/ 4 levels \"Good_Privacy\",..: NA 3 NA NA 3 NA NA NA NA NA ... #>  $ Fireplace_Qu      : Factor w/ 5 levels \"Excellent\",\"Fair\",..: 3 NA NA 5 5 3 NA NA 5 5 ... #>  $ Fireplaces        : int  2 0 0 2 1 1 0 0 1 1 ... #>  $ First_Flr_SF      : int  1656 896 1329 2110 928 926 1338 1280 1616 1028 ... #>  $ Foundation        : Factor w/ 6 levels \"BrkTil\",\"CBlock\",..: 2 2 2 2 3 3 3 3 3 3 ... #>  $ Full_Bath         : int  1 1 1 2 2 2 2 2 2 2 ... #>  $ Functional        : Factor w/ 8 levels \"Maj1\",\"Maj2\",..: 8 8 8 8 8 8 8 8 8 8 ... #>  $ Garage_Area       : int  528 730 312 522 482 470 582 506 608 442 ... #>  $ Garage_Cars       : int  2 1 1 2 2 2 2 2 2 2 ... #>  $ Garage_Cond       : Factor w/ 5 levels \"Excellent\",\"Fair\",..: 5 5 5 5 5 5 5 5 5 5 ... #>  $ Garage_Finish     : Factor w/ 3 levels \"Fin\",\"RFn\",\"Unf\": 1 3 3 1 1 1 1 2 2 1 ... #>  $ Garage_Qual       : Factor w/ 5 levels \"Excellent\",\"Fair\",..: 5 5 5 5 5 5 5 5 5 5 ... #>  $ Garage_Type       : Factor w/ 6 levels \"Attchd\",\"Basment\",..: 1 1 1 1 1 1 1 1 1 1 ... #>  $ Garage_Yr_Blt     : int  1960 1961 1958 1968 1997 1998 2001 1992 1995 1999 ... #>  $ Gr_Liv_Area       : int  1656 896 1329 2110 1629 1604 1338 1280 1616 1804 ... #>  $ Half_Bath         : int  0 0 1 1 1 1 0 0 0 1 ... #>  $ Heating           : Factor w/ 6 levels \"Floor\",\"GasA\",..: 2 2 2 2 2 2 2 2 2 2 ... #>  $ Heating_QC        : Factor w/ 5 levels \"Excellent\",\"Fair\",..: 2 5 5 1 3 1 1 1 1 3 ... #>  $ House_Style       : Factor w/ 8 levels \"One_Story\",\"One_and_Half_Fin\",..: 1 1 1 1 6 6 1 1 1 6 ... #>  $ Kitchen_AbvGr     : int  1 1 1 1 1 1 1 1 1 1 ... #>  $ Land_Contour      : Factor w/ 4 levels \"Bnk\",\"HLS\",\"Low\",..: 4 4 4 4 4 4 4 2 4 4 ... #>  $ Land_Slope        : Factor w/ 3 levels \"Gtl\",\"Mod\",\"Sev\": 1 1 1 1 1 1 1 1 1 1 ... #>  $ Lot_Area          : int  31770 11622 14267 11160 13830 9978 4920 5005 5389 7500 ... #>  $ Lot_Area_m2       : num  2952 1080 1325 1037 1285 ... #>  $ Lot_Config        : Factor w/ 5 levels \"Corner\",\"CulDSac\",..: 1 5 1 1 5 5 5 5 5 5 ... #>  $ Lot_Frontage      : int  141 80 81 93 74 78 41 43 39 60 ... #>  $ Lot_Shape         : Factor w/ 4 levels \"Irregular\",\"Moderately_Irregular\",..: 4 3 4 3 4 4 3 4 4 3 ... #>  $ Low_Qual_Fin_SF   : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ Mas_Vnr_Area      : int  112 0 108 0 0 20 0 0 0 0 ... #>  $ Mas_Vnr_Type      : Factor w/ 5 levels \"BrkCmn\",\"BrkFace\",..: 5 4 2 4 4 2 4 4 4 4 ... #>  $ Misc_Feature      : Factor w/ 5 levels \"Elev\",\"Gar2\",..: NA NA 2 NA NA NA NA NA NA NA ... #>  $ Misc_Feature_2    : Factor w/ 1 level \"Othr\": 1 1 1 1 1 1 1 1 1 1 ... #>  $ Misc_Val          : int  0 0 12500 0 0 0 0 0 0 0 ... #>  $ Mo_Sold           : int  5 6 6 4 3 6 4 1 3 6 ... #>  $ MS_SubClass       : Factor w/ 16 levels \"Duplex_All_Styles_and_Ages\",..: 3 3 3 3 14 14 4 4 4 14 ... #>  $ MS_Zoning         : Factor w/ 7 levels \"A_agr\",\"C_all\",..: 6 5 6 6 6 6 6 6 6 6 ... #>  $ Neighborhood      : Factor w/ 28 levels \"Bloomington_Heights\",..: 16 16 16 16 9 9 26 26 26 9 ... #>  $ Open_Porch_SF     : int  62 0 36 0 34 36 0 82 152 60 ... #>  $ Overall_Cond      : Factor w/ 9 levels \"Above_Average\",..: 2 1 1 2 2 1 2 2 2 2 ... #>  $ Overall_Qual      : Factor w/ 10 levels \"Above_Average\",..: 1 2 1 6 2 1 9 9 9 6 ... #>  $ Paved_Drive       : Factor w/ 3 levels \"Dirt_Gravel\",..: 2 3 3 3 3 3 3 3 3 3 ... #>  $ Pool_Area         : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ Pool_QC           : Factor w/ 4 levels \"Excellent\",\"Fair\",..: NA NA NA NA NA NA NA NA NA NA ... #>  $ Roof_Matl         : Factor w/ 8 levels \"ClyTile\",\"CompShg\",..: 2 2 2 2 2 2 2 2 2 2 ... #>  $ Roof_Style        : Factor w/ 6 levels \"Flat\",\"Gable\",..: 4 2 4 4 2 2 2 2 2 2 ... #>  $ Sale_Condition    : Factor w/ 6 levels \"Abnorml\",\"AdjLand\",..: 5 5 5 5 5 5 5 5 5 5 ... #>  $ Sale_Type         : Factor w/ 10 levels \"COD\",\"CWD\",\"Con\",..: 10 10 10 10 10 10 10 10 10 10 ... #>  $ Screen_Porch      : int  0 120 0 0 0 0 0 144 0 0 ... #>  $ Second_Flr_SF     : int  0 0 0 0 701 678 0 0 0 776 ... #>  $ Street            : Factor w/ 2 levels \"Grvl\",\"Pave\": 2 2 2 2 2 2 2 2 2 2 ... #>  $ Three_season_porch: int  0 0 0 0 0 0 0 0 0 0 ... #>  $ Total_Bsmt_SF     : int  1080 882 1329 2110 928 926 1338 1280 1595 994 ... #>  $ TotRms_AbvGrd     : int  7 5 6 8 6 7 6 5 5 7 ... #>  $ Utilities         : Factor w/ 3 levels \"AllPub\",\"NoSeWa\",..: 1 1 1 1 1 1 1 1 1 1 ... #>  $ Wood_Deck_SF      : int  210 140 393 0 212 360 0 0 237 140 ... #>  $ Year_Built        : int  1960 1961 1958 1968 1997 1998 2001 1992 1995 1999 ... #>  $ Year_Remod_Add    : int  1960 1961 1958 1968 1998 1998 2001 1992 1996 1999 ... #>  $ Year_Sold         : int  2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 ... #>  - attr(*, \".internal.selfref\")=<externalptr>"},{"path":"https://mlr3data.mlr-org.com/dev/reference/bike_sharing.html","id":null,"dir":"Reference","previous_headings":"","what":"Bike Sharing Demand — bike_sharing","title":"Bike Sharing Demand — bike_sharing","text":"Regression data predict total count bikes rented. Contains 13 features 17379 observations. Target column \"count\".","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/bike_sharing.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Bike Sharing Demand — bike_sharing","text":"https://archive.ics.uci.edu/ml/datasets/bike+sharing+dataset","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/bike_sharing.html","id":"pre-processing","dir":"Reference","previous_headings":"","what":"Pre-processing","title":"Bike Sharing Demand — bike_sharing","text":"columns renamed. instant, \"registered\" \"casual\" column removed. \"season\" \"weather\" converted factor(). \"holiday\" \"working_day\" converted logical().","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/bike_sharing.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bike Sharing Demand — bike_sharing","text":"","code":"data(\"bike_sharing\", package = \"mlr3data\") str(bike_sharing) #> Classes ‘data.table’ and 'data.frame':\t17379 obs. of  14 variables: #>  $ date                : chr  \"2011-01-01\" \"2011-01-01\" \"2011-01-01\" \"2011-01-01\" ... #>  $ season              : Factor w/ 4 levels \"winter\",\"spring\",..: 1 1 1 1 1 1 1 1 1 1 ... #>  $ year                : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ month               : int  1 1 1 1 1 1 1 1 1 1 ... #>  $ hour                : int  0 1 2 3 4 5 6 7 8 9 ... #>  $ holiday             : logi  FALSE FALSE FALSE FALSE FALSE FALSE ... #>  $ weekday             : int  6 6 6 6 6 6 6 6 6 6 ... #>  $ working_day         : logi  FALSE FALSE FALSE FALSE FALSE FALSE ... #>  $ weather             : Factor w/ 4 levels \"1\",\"2\",\"3\",\"4\": 1 1 1 1 1 2 1 1 1 1 ... #>  $ temperature         : num  0.24 0.22 0.22 0.24 0.24 0.24 0.22 0.2 0.24 0.32 ... #>  $ apparent_temperature: num  0.288 0.273 0.273 0.288 0.288 ... #>  $ humidity            : num  0.81 0.8 0.8 0.75 0.75 0.75 0.8 0.86 0.75 0.76 ... #>  $ windspeed           : num  0 0 0 0 0 0.0896 0 0 0 0 ... #>  $ count               : int  16 40 32 13 1 1 2 3 8 14 ... #>  - attr(*, \".internal.selfref\")=<externalptr>"},{"path":"https://mlr3data.mlr-org.com/dev/reference/energy_usage.html","id":null,"dir":"Reference","previous_headings":"","what":"Power Consumption of Kitchen Appliances in Ames, Iowa — energy_usage","title":"Power Consumption of Kitchen Appliances in Ames, Iowa — energy_usage","text":"Data power consumption kitchen appliances Ames, Iowa. Extends ames_housing data set. Contains 720 features 2930 observations.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/energy_usage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Power Consumption of Kitchen Appliances in Ames, Iowa — energy_usage","text":"","code":"data(\"energy_usage\", package = \"mlr3data\") str(energy_usage) #> Classes ‘data.table’ and 'data.frame':\t2930 obs. of  720 variables: #>  $ att1  : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att2  : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att3  : num  0.715 0.267 0.48 0.599 0.249 ... #>  $ att4  : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att5  : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att6  : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att7  : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att8  : num  0.715 0.267 0.464 0.503 0.249 ... #>  $ att9  : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att10 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att11 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att12 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att13 : num  0.715 0.267 0.464 0.599 0.249 ... #>  $ att14 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att15 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att16 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att17 : num  0.715 0.267 0.633 0.599 0.345 ... #>  $ att18 : num  0.715 0.267 0.48 0.599 0.249 ... #>  $ att19 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att20 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att21 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att22 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att23 : num  0.715 0.267 0.464 0.599 0.249 ... #>  $ att24 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att25 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att26 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att27 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att28 : num  0.715 0.267 0.464 0.503 0.249 ... #>  $ att29 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att30 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att31 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att32 : num  0.715 0.267 0.48 0.503 0.345 ... #>  $ att33 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att34 : num  0.715 0.267 0.464 0.599 0.249 ... #>  $ att35 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att36 : num  0.715 0.267 0.48 0.503 0.345 ... #>  $ att37 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att38 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att39 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att40 : num  0.715 0.267 0.464 0.503 0.249 ... #>  $ att41 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att42 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att43 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att44 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att45 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att46 : num  0.715 0.267 0.633 0.599 0.345 ... #>  $ att47 : num  0.715 0.267 0.48 0.599 0.249 ... #>  $ att48 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att49 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att50 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att51 : num  0.715 0.267 0.464 0.599 0.249 ... #>  $ att52 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att53 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att54 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att55 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att56 : num  0.715 0.267 0.464 0.599 0.249 ... #>  $ att57 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att58 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att59 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att60 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att61 : num  0.715 0.267 0.464 0.599 0.249 ... #>  $ att62 : num  0.715 0.267 0.48 0.503 0.345 ... #>  $ att63 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att64 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att65 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att66 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att67 : num  0.715 0.267 0.464 0.599 0.249 ... #>  $ att68 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att69 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att70 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att71 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att72 : num  0.715 0.267 0.464 0.599 0.249 ... #>  $ att73 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att74 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att75 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att76 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att77 : num  0.715 0.267 0.48 0.599 0.249 ... #>  $ att78 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att79 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att80 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att81 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att82 : num  0.715 0.267 0.464 0.599 0.249 ... #>  $ att83 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att84 : num  0.715 0.267 0.48 0.503 0.345 ... #>  $ att85 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att86 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att87 : num  0.715 0.267 0.633 0.599 0.249 ... #>  $ att88 : num  0.715 0.267 0.48 0.503 0.345 ... #>  $ att89 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att90 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att91 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att92 : num  0.715 0.267 0.48 0.503 0.249 ... #>  $ att93 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att94 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att95 : num  0.715 0.267 0.48 0.599 0.345 ... #>  $ att96 : num  0.715 0.267 0.464 0.599 0.345 ... #>  $ att97 : num  0.715 0.267 0.464 0.503 0.345 ... #>  $ att98 : num  0.715 0.267 0.464 0.599 0.249 ... #>  $ att99 : num  0.715 0.267 0.48 0.599 0.345 ... #>   [list output truncated] #>  - attr(*, \".internal.selfref\")=<externalptr>"},{"path":"https://mlr3data.mlr-org.com/dev/reference/ilpd.html","id":null,"dir":"Reference","previous_headings":"","what":"Indian Liver Patient Dataset — ilpd","title":"Indian Liver Patient Dataset — ilpd","text":"Classification data predict whether person liver patient. Obtained using mlr3oml package. Contains 538 observations 10 features. Target column \"diseased\".","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/ilpd.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Indian Liver Patient Dataset — ilpd","text":"https://www.openml.org/d/1480","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/ilpd.html","id":"pre-processing","dir":"Reference","previous_headings":"","what":"Pre-processing","title":"Indian Liver Patient Dataset — ilpd","text":"variables renamed. target variable re-encoded \"yes\" \"\".","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/ilpd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Indian Liver Patient Dataset — ilpd","text":"","code":"data(\"ilpd\", package = \"mlr3data\") str(ilpd) #> 'data.frame':\t583 obs. of  11 variables: #>  $ age                   : int  65 62 62 58 72 46 26 29 17 55 ... #>  $ gender                : Factor w/ 2 levels \"Female\",\"Male\": 1 2 2 2 2 2 1 1 2 2 ... #>  $ total_bilirubin       : num  0.7 10.9 7.3 1 3.9 1.8 0.9 0.9 0.9 0.7 ... #>  $ direct_bilirubin      : num  0.1 5.5 4.1 0.4 2 0.7 0.2 0.3 0.3 0.2 ... #>  $ alkaline_phosphatase  : int  187 699 490 182 195 208 154 202 202 290 ... #>  $ alanine_transaminase  : int  16 64 60 14 27 19 16 14 22 53 ... #>  $ aspartate_transaminase: int  18 100 68 20 59 14 12 11 19 58 ... #>  $ total_protein         : num  6.8 7.5 7 6.8 7.3 7.6 7 6.7 7.4 6.8 ... #>  $ albumin               : num  3.3 3.2 3.3 3.4 2.4 4.4 3.5 3.6 4.1 3.4 ... #>  $ albumin_globulin_ratio: num  0.9 0.74 0.89 1 0.4 1.3 1 1.1 1.2 1 ... #>  $ diseased              : Factor w/ 2 levels \"yes\",\"no\": 1 1 1 1 1 1 1 1 2 1 ..."},{"path":"https://mlr3data.mlr-org.com/dev/reference/kc_housing.html","id":null,"dir":"Reference","previous_headings":"","what":"House Sales in King County — kc_housing","title":"House Sales in King County — kc_housing","text":"Regression task predict house sale prices King County, including Seattle, May 2014 May 2015. Contains 19 features 21613 observations. Target column \"price\".","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/kc_housing.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"House Sales in King County — kc_housing","text":"https://www.kaggle.com/datasets/harlfoxem/housesalesprediction","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/kc_housing.html","id":"pre-processing","dir":"Reference","previous_headings":"","what":"Pre-processing","title":"House Sales in King County — kc_housing","text":"Id column removed. Dates column \"date\" converted strings POSIXct. Values 0 feature \"yr_renovated\" replaced NA. Values 0 feature \"sqft_basement\" replaced NA. Feature \"waterfront\" converted logical.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/kc_housing.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"House Sales in King County — kc_housing","text":"","code":"data(\"kc_housing\", package = \"mlr3data\") str(kc_housing) #> 'data.frame':\t21613 obs. of  20 variables: #>  $ date         : POSIXct, format: \"2014-10-13\" \"2014-12-09\" ... #>  $ price        : num  221900 538000 180000 604000 510000 ... #>  $ bedrooms     : int  3 3 2 4 3 4 3 3 3 3 ... #>  $ bathrooms    : num  1 2.25 1 3 2 4.5 2.25 1.5 1 2.5 ... #>  $ sqft_living  : int  1180 2570 770 1960 1680 5420 1715 1060 1780 1890 ... #>  $ sqft_lot     : int  5650 7242 10000 5000 8080 101930 6819 9711 7470 6560 ... #>  $ floors       : num  1 2 1 1 1 1 2 1 1 2 ... #>  $ waterfront   : logi  FALSE FALSE FALSE FALSE FALSE FALSE ... #>  $ view         : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ condition    : int  3 3 3 5 3 3 3 3 3 3 ... #>  $ grade        : int  7 7 6 7 8 11 7 7 7 7 ... #>  $ sqft_above   : int  1180 2170 770 1050 1680 3890 1715 1060 1050 1890 ... #>  $ sqft_basement: int  NA 400 NA 910 NA 1530 NA NA 730 NA ... #>  $ yr_built     : int  1955 1951 1933 1965 1987 2001 1995 1963 1960 2003 ... #>  $ yr_renovated : int  NA 1991 NA NA NA NA NA NA NA NA ... #>  $ zipcode      : int  98178 98125 98028 98136 98074 98053 98003 98198 98146 98038 ... #>  $ lat          : num  47.5 47.7 47.7 47.5 47.6 ... #>  $ long         : num  -122 -122 -122 -122 -122 ... #>  $ sqft_living15: int  1340 1690 2720 1360 1800 4760 2238 1650 1780 2390 ... #>  $ sqft_lot15   : int  5650 7639 8062 5000 7503 101930 6819 9711 8113 7570 ... #>  - attr(*, \"index\")= int(0)"},{"path":"https://mlr3data.mlr-org.com/dev/reference/mlr3data-package.html","id":null,"dir":"Reference","previous_headings":"","what":"mlr3data: Collection of Machine Learning Data Sets for 'mlr3' — mlr3data-package","title":"mlr3data: Collection of Machine Learning Data Sets for 'mlr3' — mlr3data-package","text":"small collection interesting educational machine learning data sets used examples 'mlr3' book (https://mlr3book.mlr-org.com), use case gallery (https://mlr3gallery.mlr-org.com), examples. data sets properly preprocessed ready analyzed machine learning algorithms. Data sets automatically added dictionary tasks 'mlr3' loaded.","code":""},{"path":[]},{"path":"https://mlr3data.mlr-org.com/dev/reference/mlr3data-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"mlr3data: Collection of Machine Learning Data Sets for 'mlr3' — mlr3data-package","text":"Maintainer: Marc Becker marcbecker@posteo.de (ORCID) contributors: Michel Lang michellang@gmail.com (ORCID) [contributor]","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/moneyball.html","id":null,"dir":"Reference","previous_headings":"","what":"Major League Baseball Statistics 1962-2012 — moneyball","title":"Major League Baseball Statistics 1962-2012 — moneyball","text":"Regression data predict number runs scored. Obtained using mlr3oml package. Contains 14 features 1232 observations. Target column \"rs\".","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/moneyball.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Major League Baseball Statistics 1962-2012 — moneyball","text":"https://www.openml.org/d/41021","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/moneyball.html","id":"pre-processing","dir":"Reference","previous_headings":"","what":"Pre-processing","title":"Major League Baseball Statistics 1962-2012 — moneyball","text":"variable names converted upper case lower case. variables \"year\", \"rs\", \"ra\", \"w\"` coerced integers.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/moneyball.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Major League Baseball Statistics 1962-2012 — moneyball","text":"","code":"data(\"moneyball\", package = \"mlr3data\") str(moneyball) #> 'data.frame':\t1232 obs. of  15 variables: #>  $ team        : Factor w/ 39 levels \"ARI\",\"ATL\",\"BAL\",..: 1 2 3 4 5 6 7 8 9 10 ... #>  $ league      : Factor w/ 2 levels \"AL\",\"NL\": 2 2 1 1 2 1 2 1 2 1 ... #>  $ year        : int  2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 ... #>  $ rs          : int  734 700 712 734 613 748 669 667 758 726 ... #>  $ ra          : int  688 600 705 806 759 676 588 845 890 670 ... #>  $ w           : int  81 94 93 69 61 85 97 68 64 88 ... #>  $ obp         : num  0.328 0.32 0.311 0.315 0.302 0.318 0.315 0.324 0.33 0.335 ... #>  $ slg         : num  0.418 0.389 0.417 0.415 0.378 0.422 0.411 0.381 0.436 0.422 ... #>  $ ba          : num  0.259 0.247 0.247 0.26 0.24 0.255 0.251 0.251 0.274 0.268 ... #>  $ playoffs    : Factor w/ 2 levels \"0\",\"1\": 1 2 2 1 1 1 2 1 1 2 ... #>  $ rankseason  : Factor w/ 8 levels \"1\",\"2\",\"3\",\"4\",..: NA 4 5 NA NA NA 2 NA NA 6 ... #>  $ rankplayoffs: Factor w/ 5 levels \"1\",\"2\",\"3\",\"4\",..: NA 5 4 NA NA NA 4 NA NA 2 ... #>  $ g           : Factor w/ 8 levels \"158\",\"159\",\"160\",..: 5 5 5 5 5 5 5 5 5 5 ... #>  $ oobp        : num  0.317 0.306 0.315 0.331 0.335 0.319 0.305 0.336 0.357 0.314 ... #>  $ oslg        : num  0.415 0.378 0.403 0.428 0.424 0.405 0.39 0.43 0.47 0.402 ..."},{"path":"https://mlr3data.mlr-org.com/dev/reference/optdigits.html","id":null,"dir":"Reference","previous_headings":"","what":"Optical Recognition of Handwritten Digits — optdigits","title":"Optical Recognition of Handwritten Digits — optdigits","text":"Classification data predict handwritten digits. Obtained using mlr3oml package. Binarized version original data set. multi-class target column converted two-class nominal target column re-labeling majority class positive (\"P\") others negative (\"N\"). Originally converted Quan Sun. Contains 64 features 5620 observations. Target column \"binaryclass\".","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/optdigits.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Optical Recognition of Handwritten Digits — optdigits","text":"https://www.openml.org/d/980","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/optdigits.html","id":"pre-processing","dir":"Reference","previous_headings":"","what":"Pre-processing","title":"Optical Recognition of Handwritten Digits — optdigits","text":"feature variables \"input1\", ..., \"input64\" (number pixels block) coerced integers. target variable renamed \"binaryClass\" \"binaryclass\".","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/optdigits.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Optical Recognition of Handwritten Digits — optdigits","text":"","code":"data(\"optdigits\", package = \"mlr3data\") str(optdigits) #> 'data.frame':\t5620 obs. of  65 variables: #>  $ input1     : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input2     : int  1 0 0 0 0 0 0 0 0 0 ... #>  $ input3     : int  6 10 8 0 5 11 1 8 15 3 ... #>  $ input4     : int  15 16 15 3 14 16 11 10 2 13 ... #>  $ input5     : int  12 6 16 11 4 10 13 8 14 13 ... #>  $ input6     : int  1 0 13 16 0 1 11 7 13 2 ... #>  $ input7     : int  0 0 0 0 0 0 7 2 2 0 ... #>  $ input8     : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input9     : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input10    : int  7 7 1 0 0 4 0 1 0 6 ... #>  $ input11    : int  16 16 11 5 13 16 9 15 16 16 ... #>  $ input12    : int  6 8 9 16 8 10 14 14 15 12 ... #>  $ input13    : int  6 16 11 11 0 15 6 12 12 10 ... #>  $ input14    : int  10 5 16 13 0 8 4 12 13 8 ... #>  $ input15    : int  0 0 1 7 0 0 3 4 8 0 ... #>  $ input16    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input17    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input18    : int  8 11 0 3 3 4 0 7 2 9 ... #>  $ input19    : int  16 16 0 15 14 16 16 15 16 15 ... #>  $ input20    : int  2 0 0 8 4 3 12 12 12 12 ... #>  $ input21    : int  0 6 7 1 0 11 16 5 1 16 ... #>  $ input22    : int  11 14 14 15 0 13 15 0 6 6 ... #>  $ input23    : int  2 3 0 6 0 0 2 0 10 0 ... #>  $ input24    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input25    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input26    : int  5 12 0 11 6 1 5 5 7 10 ... #>  $ input27    : int  16 12 3 16 16 14 16 14 15 16 ... #>  $ input28    : int  3 0 4 16 14 6 10 12 3 16 ... #>  $ input29    : int  0 0 14 16 9 9 4 15 0 13 ... #>  $ input30    : int  5 11 12 16 2 14 12 7 5 0 ... #>  $ input31    : int  7 11 2 10 0 0 6 0 8 0 ... #>  $ input32    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input33    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input34    : int  7 12 1 1 4 0 1 0 5 1 ... #>  $ input35    : int  13 12 16 4 16 0 1 0 12 12 ... #>  $ input36    : int  3 0 16 4 3 0 0 0 0 16 ... #>  $ input37    : int  0 0 16 13 4 12 0 2 0 12 ... #>  $ input38    : int  8 8 16 10 11 10 10 13 8 14 ... #>  $ input39    : int  7 12 10 2 2 0 4 0 8 4 ... #>  $ input40    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input41    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input42    : int  4 7 2 0 0 0 0 0 5 0 ... #>  $ input43    : int  12 15 12 0 14 0 0 0 12 11 ... #>  $ input44    : int  0 1 16 0 3 6 0 0 0 8 ... #>  $ input45    : int  1 0 10 15 0 16 5 4 7 0 ... #>  $ input46    : int  13 13 0 4 4 6 10 12 15 3 ... #>  $ input47    : int  5 11 0 0 11 0 0 0 5 12 ... #>  $ input48    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input49    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input50    : int  0 0 0 0 0 0 0 0 5 0 ... #>  $ input51    : int  14 16 2 0 10 5 0 6 16 13 ... #>  $ input52    : int  9 8 16 3 8 15 8 7 13 11 ... #>  $ input53    : int  15 10 4 16 4 15 15 14 16 8 ... #>  $ input54    : int  9 15 0 0 11 8 3 5 6 13 ... #>  $ input55    : int  0 3 0 0 12 8 0 0 0 12 ... #>  $ input56    : int  0 0 0 0 0 3 0 0 0 0 ... #>  $ input57    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input58    : int  0 0 0 0 0 0 0 0 0 0 ... #>  $ input59    : int  6 10 9 0 4 10 1 4 10 3 ... #>  $ input60    : int  14 16 14 1 12 16 13 13 12 15 ... #>  $ input61    : int  7 15 0 15 14 16 5 8 5 11 ... #>  $ input62    : int  1 3 0 2 7 16 0 0 0 6 ... #>  $ input63    : int  0 0 0 0 0 16 0 0 0 0 ... #>  $ input64    : int  0 0 0 0 0 6 0 0 0 0 ... #>  $ binaryclass: Factor w/ 2 levels \"P\",\"N\": 2 2 2 2 2 2 2 2 2 2 ..."},{"path":"https://mlr3data.mlr-org.com/dev/reference/penguins_simple.html","id":null,"dir":"Reference","previous_headings":"","what":"Simplified Palmer Penguins Data Set — penguins_simple","title":"Simplified Palmer Penguins Data Set — penguins_simple","text":"Classification data predict species penguins palmerpenguins package. better alternative iris data set.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/penguins_simple.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Simplified Palmer Penguins Data Set — penguins_simple","text":"palmerpenguins","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/penguins_simple.html","id":"pre-processing","dir":"Reference","previous_headings":"","what":"Pre-processing","title":"Simplified Palmer Penguins Data Set — penguins_simple","text":"unit measurement removed column names. Lengths given millimeters (mm), weight gram (g). Observations missing values removed. Factor variables one-hot encoded.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/penguins_simple.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Simplified Palmer Penguins Data Set — penguins_simple","text":"Gorman KB, Williams TD, Fraser WR (2014). “Ecological Sexual Dimorphism Environmental Variability within Community Antarctic Penguins (Genus Pygoscelis).” PLoS ONE, 9(3), e90081. doi:10.1371/journal.pone.0090081 . https://github.com/allisonhorst/palmerpenguins","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/penguins_simple.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simplified Palmer Penguins Data Set — penguins_simple","text":"","code":"data(\"penguins_simple\", package = \"mlr3data\") str(penguins_simple) #> 'data.frame':\t333 obs. of  11 variables: #>  $ species         : Factor w/ 3 levels \"Adelie\",\"Chinstrap\",..: 1 1 1 1 1 1 1 1 1 1 ... #>  $ bill_depth      : num  18.7 17.4 18 19.3 20.6 17.8 19.6 17.6 21.2 21.1 ... #>  $ bill_length     : num  39.1 39.5 40.3 36.7 39.3 38.9 39.2 41.1 38.6 34.6 ... #>  $ body_mass       : int  3750 3800 3250 3450 3650 3625 4675 3200 3800 4400 ... #>  $ flipper_length  : int  181 186 195 193 190 181 195 182 191 198 ... #>  $ year            : int  2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 ... #>  $ island.Biscoe   : num  0 0 0 0 0 0 0 0 0 0 ... #>  $ island.Dream    : num  0 0 0 0 0 0 0 0 0 0 ... #>  $ island.Torgersen: num  1 1 1 1 1 1 1 1 1 1 ... #>  $ sex.female      : num  0 1 1 1 0 1 0 1 0 0 ... #>  $ sex.male        : num  1 0 0 0 1 0 1 0 1 1 ..."},{"path":"https://mlr3data.mlr-org.com/dev/reference/titanic.html","id":null,"dir":"Reference","previous_headings":"","what":"Titanic — titanic","title":"Titanic — titanic","text":"Classification data predict fate passengers ocean liner \"Titanic\". Contains 10 features 1309 observations. Target column \"Survived\".","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/titanic.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Titanic — titanic","text":"titanic https://www.kaggle.com/c/titanic/data","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/titanic.html","id":"pre-processing","dir":"Reference","previous_headings":"","what":"Pre-processing","title":"Titanic — titanic","text":"column names changed snake_case. training test set joined. Observations test set missing value target column \"survived\". Column '\"survived\"' re-encoded factor levels '\"yes\"' '\"\"'. Id column removed. Passenger class \"pclass\" converted ordered factor. Features \"sex\" \"embarked\" converted factors. Empty strings \"cabin\" \"embarked\" encoded missing values.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/reference/titanic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Titanic — titanic","text":"","code":"data(\"titanic\", package = \"mlr3data\") str(titanic) #> 'data.frame':\t1309 obs. of  11 variables: #>  $ survived: Factor w/ 2 levels \"yes\",\"no\": 2 1 1 1 2 2 2 2 1 1 ... #>  $ pclass  : Ord.factor w/ 3 levels \"1\"<\"2\"<\"3\": 3 1 3 1 3 3 1 3 3 2 ... #>  $ name    : chr  \"Braund, Mr. Owen Harris\" \"Cumings, Mrs. John Bradley (Florence Briggs Thayer)\" \"Heikkinen, Miss. Laina\" \"Futrelle, Mrs. Jacques Heath (Lily May Peel)\" ... #>  $ sex     : Factor w/ 2 levels \"female\",\"male\": 2 1 1 1 2 2 2 2 1 1 ... #>  $ age     : num  22 38 26 35 35 NA 54 2 27 14 ... #>  $ sib_sp  : int  1 1 0 1 0 0 0 3 0 1 ... #>  $ parch   : int  0 0 0 0 0 0 0 1 2 0 ... #>  $ ticket  : chr  \"A/5 21171\" \"PC 17599\" \"STON/O2. 3101282\" \"113803\" ... #>  $ fare    : num  7.25 71.28 7.92 53.1 8.05 ... #>  $ cabin   : chr  NA \"C85\" NA \"C123\" ... #>  $ embarked: Factor w/ 3 levels \"C\",\"Q\",\"S\": 3 1 3 3 3 2 3 3 3 1 ..."},{"path":"https://mlr3data.mlr-org.com/dev/news/index.html","id":"mlr3data-development-version","dir":"Changelog","previous_headings":"","what":"mlr3data (development version)","title":"mlr3data (development version)","text":"Readd ames_housing data set.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/news/index.html","id":"mlr3data-080","dir":"Changelog","previous_headings":"","what":"mlr3data 0.8.0","title":"mlr3data 0.8.0","text":"CRAN release: 2024-11-06 Move ames_housing mlr3.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/news/index.html","id":"mlr3data-070","dir":"Changelog","previous_headings":"","what":"mlr3data 0.7.0","title":"mlr3data 0.7.0","text":"CRAN release: 2023-06-29 Added dataset ames_housing, used book mlr3. Added dataset energy_usage, used book mlr3.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/news/index.html","id":"mlr3data-061","dir":"Changelog","previous_headings":"","what":"mlr3data 0.6.1","title":"mlr3data 0.6.1","text":"CRAN release: 2022-08-15 Fixed documentation CRAN notes. Added simplified version penguins data set penguins_simple. Added labels data sets.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/news/index.html","id":"mlr3data-060","dir":"Changelog","previous_headings":"","what":"mlr3data 0.6.0","title":"mlr3data 0.6.0","text":"CRAN release: 2022-03-18 Added simplified version penguins data set penguins_simple. Added labels data sets.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/news/index.html","id":"mlr3data-050","dir":"Changelog","previous_headings":"","what":"mlr3data 0.5.0","title":"mlr3data 0.5.0","text":"CRAN release: 2021-06-29 Added bike sharing regression task UCI. mlr3 tasks now loaded lazily.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/news/index.html","id":"mlr3data-040","dir":"Changelog","previous_headings":"","what":"mlr3data 0.4.0","title":"mlr3data 0.4.0","text":"CRAN release: 2021-06-08 Added Indian liver patient dataset ilpd.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/news/index.html","id":"mlr3data-031","dir":"Changelog","previous_headings":"","what":"mlr3data 0.3.1","title":"mlr3data 0.3.1","text":"CRAN release: 2021-03-19 Removed penguins data set; now included mlr3.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/news/index.html","id":"mlr3data-030","dir":"Changelog","previous_headings":"","what":"mlr3data 0.3.0","title":"mlr3data 0.3.0","text":"CRAN release: 2020-12-04 Added optdigits data obtained via OpenML package. Added moneyball data obtained via OpenML package.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/news/index.html","id":"mlr3data-021","dir":"Changelog","previous_headings":"","what":"mlr3data 0.2.1","title":"mlr3data 0.2.1","text":"CRAN release: 2020-10-05 Removed dependency orphaned package bibtex.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/news/index.html","id":"mlr3data-020","dir":"Changelog","previous_headings":"","what":"mlr3data 0.2.0","title":"mlr3data 0.2.0","text":"CRAN release: 2020-08-03 Data sets now automatically added task dictionary mlr3 loaded. Added penguins data palmerpenguins package.","code":""},{"path":"https://mlr3data.mlr-org.com/dev/news/index.html","id":"mlr3data-010","dir":"Changelog","previous_headings":"","what":"mlr3data 0.1.0","title":"mlr3data 0.1.0","text":"CRAN release: 2020-02-10 Initial release.","code":""}]
diff --git a/dev/sitemap.xml b/dev/sitemap.xml
index 5978711..0a45007 100644
--- a/dev/sitemap.xml
+++ b/dev/sitemap.xml
@@ -3,6 +3,7 @@
 <url><loc>https://mlr3data.mlr-org.com/dev/authors.html</loc></url>
 <url><loc>https://mlr3data.mlr-org.com/dev/index.html</loc></url>
 <url><loc>https://mlr3data.mlr-org.com/dev/news/index.html</loc></url>
+<url><loc>https://mlr3data.mlr-org.com/dev/reference/ames_housing.html</loc></url>
 <url><loc>https://mlr3data.mlr-org.com/dev/reference/bike_sharing.html</loc></url>
 <url><loc>https://mlr3data.mlr-org.com/dev/reference/energy_usage.html</loc></url>
 <url><loc>https://mlr3data.mlr-org.com/dev/reference/ilpd.html</loc></url>
