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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Anomaly detection\n",
+    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/yggdrasil-decision-forests/blob/main/documentation/public/docs/tutorial/anomaly_detection.ipynb)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install ydf ucimlrepo scikit-learn umap-learn plotly -U -q"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What is anomaly detection?\n",
+    "\n",
+    "**Anomaly detection** techniques are non-supervised learning algorithms for identifying rare and unusual patterns in data that deviate significantly from the norm.\n",
+    "For example, anomaly detection can be used for fraud detection, network intrusion detection, and fault diagnosis, without the need for defining of abnormal instances.\n",
+    "\n",
+    "Anomaly detection with decision forests is a straightforward but effective technique for tabular data. The model assigns an anomaly score to each data point, ranging from 0 (normal) to 1 (abnormal). Decision forests also offer interpretability tools and properties, making it easier to understand and characterize detected anomalies.\n",
+    "\n",
+    "In anomaly detection, labeled examples are used not for training but for evaluating the model. These labels ensure that the model can detect known anomalies.\n",
+    "\n",
+    "\n",
+    "We train and evaluate two anomaly detection models on the UCI Covertype dataset, which describes forest cover types and other geographic attributes of land cells. The first model is trained on pine and willow data. Given that willow is rarer than pine, the model differentiates between them without labels. This first model will then be interpreted and characterize what constitute a pine cover type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/google/home/gbm/my_venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "2024-06-17 13:06:13.648825: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2024-06-17 13:06:14.292005: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load libraries\n",
+    "import ydf  # For learning the anomaly detection model\n",
+    "import pandas as pd  # We use Pandas to load small datasets\n",
+    "from sklearn import metrics  # Use sklearn to compute AUC\n",
+    "from ucimlrepo import fetch_ucirepo  # To download the dataset\n",
+    "import matplotlib.pyplot as plt  # For plotting\n",
+    "import seaborn as sns  # For plotting\n",
+    "import umap  # For projecting distances in 2d\n",
+    "\n",
+    "# For interactive plots\n",
+    "import plotly.graph_objs as go\n",
+    "from plotly.offline import iplot\n",
+    "import plotly.io as pio\n",
+    "pio.renderers.default=\"colab\"\n",
+    "\n",
+    "# Disable Pandas warnings\n",
+    "pd.options.mode.chained_assignment = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We download the Covertype dataset from UCI."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prepare dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# https://archive.ics.uci.edu/dataset/31/covertype\n",
+    "covertype_repo = fetch_ucirepo(id=31)\n",
+    "raw_dataset = pd.concat([covertype_repo.data.features, covertype_repo.data.targets], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Select the columns of interest and clean the labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Elevation</th>\n",
+       "      <th>Aspect</th>\n",
+       "      <th>Slope</th>\n",
+       "      <th>Horizontal_Distance_To_Hydrology</th>\n",
+       "      <th>Vertical_Distance_To_Hydrology</th>\n",
+       "      <th>Horizontal_Distance_To_Roadways</th>\n",
+       "      <th>Hillshade_9am</th>\n",
+       "      <th>Hillshade_Noon</th>\n",
+       "      <th>Hillshade_3pm</th>\n",
+       "      <th>Horizontal_Distance_To_Fire_Points</th>\n",
+       "      <th>Cover_Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2596</td>\n",
+       "      <td>51</td>\n",
+       "      <td>3</td>\n",
+       "      <td>258</td>\n",
+       "      <td>0</td>\n",
+       "      <td>510</td>\n",
+       "      <td>221</td>\n",
+       "      <td>232</td>\n",
+       "      <td>148</td>\n",
+       "      <td>6279</td>\n",
+       "      <td>Aspen</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2590</td>\n",
+       "      <td>56</td>\n",
+       "      <td>2</td>\n",
+       "      <td>212</td>\n",
+       "      <td>-6</td>\n",
+       "      <td>390</td>\n",
+       "      <td>220</td>\n",
+       "      <td>235</td>\n",
+       "      <td>151</td>\n",
+       "      <td>6225</td>\n",
+       "      <td>Aspen</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2804</td>\n",
+       "      <td>139</td>\n",
+       "      <td>9</td>\n",
+       "      <td>268</td>\n",
+       "      <td>65</td>\n",
+       "      <td>3180</td>\n",
+       "      <td>234</td>\n",
+       "      <td>238</td>\n",
+       "      <td>135</td>\n",
+       "      <td>6121</td>\n",
+       "      <td>Lodgepole Pine</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2785</td>\n",
+       "      <td>155</td>\n",
+       "      <td>18</td>\n",
+       "      <td>242</td>\n",
+       "      <td>118</td>\n",
+       "      <td>3090</td>\n",
+       "      <td>238</td>\n",
+       "      <td>238</td>\n",
+       "      <td>122</td>\n",
+       "      <td>6211</td>\n",
+       "      <td>Lodgepole Pine</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2595</td>\n",
+       "      <td>45</td>\n",
+       "      <td>2</td>\n",
+       "      <td>153</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>391</td>\n",
+       "      <td>220</td>\n",
+       "      <td>234</td>\n",
+       "      <td>150</td>\n",
+       "      <td>6172</td>\n",
+       "      <td>Aspen</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Elevation  Aspect  Slope  Horizontal_Distance_To_Hydrology  \\\n",
+       "0       2596      51      3                               258   \n",
+       "1       2590      56      2                               212   \n",
+       "2       2804     139      9                               268   \n",
+       "3       2785     155     18                               242   \n",
+       "4       2595      45      2                               153   \n",
+       "\n",
+       "   Vertical_Distance_To_Hydrology  Horizontal_Distance_To_Roadways  \\\n",
+       "0                               0                              510   \n",
+       "1                              -6                              390   \n",
+       "2                              65                             3180   \n",
+       "3                             118                             3090   \n",
+       "4                              -1                              391   \n",
+       "\n",
+       "   Hillshade_9am  Hillshade_Noon  Hillshade_3pm  \\\n",
+       "0            221             232            148   \n",
+       "1            220             235            151   \n",
+       "2            234             238            135   \n",
+       "3            238             238            122   \n",
+       "4            220             234            150   \n",
+       "\n",
+       "   Horizontal_Distance_To_Fire_Points      Cover_Type  \n",
+       "0                                6279           Aspen  \n",
+       "1                                6225           Aspen  \n",
+       "2                                6121  Lodgepole Pine  \n",
+       "3                                6211  Lodgepole Pine  \n",
+       "4                                6172           Aspen  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset = raw_dataset.copy()\n",
+    "\n",
+    "# Features of interest\n",
+    "features = [\"Elevation\", \"Aspect\", \"Slope\", \"Horizontal_Distance_To_Hydrology\",\n",
+    "            \"Vertical_Distance_To_Hydrology\", \"Horizontal_Distance_To_Roadways\",\n",
+    "            \"Hillshade_9am\", \"Hillshade_Noon\", \"Hillshade_3pm\",\n",
+    "            \"Horizontal_Distance_To_Fire_Points\"]\n",
+    "dataset = dataset[features + [\"Cover_Type\"]]\n",
+    "\n",
+    "# Covert type as text\n",
+    "dataset[\"Cover_Type\"] = dataset[\"Cover_Type\"].map({\n",
+    "    1: \"Spruce/Fir\",\n",
+    "    2: \"Lodgepole Pine\",\n",
+    "    3: \"Ponderosa Pine\",\n",
+    "    4: \"Cottonwood/Willow\",\n",
+    "    5: \"Aspen\",\n",
+    "    6: \"Douglas-fir\",\n",
+    "    7: \"Krummholz\"\n",
+    "})\n",
+    "\n",
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first model is trained on the \"filtered dataset\" than only contain spruce/fir and cottonwood/willow examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filtered_dataset = dataset[dataset[\"Cover_Type\"].isin([\"Spruce/Fir\", \"Cottonwood/Willow\"])]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see, the spruce/fir cover is much more common than the cottonwood/willow cover:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Cover_Type\n",
+       "Spruce/Fir           211840\n",
+       "Cottonwood/Willow      2747\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_dataset[\"Cover_Type\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We train a popular anomaly detection decision forest algorithm called isolation forest."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Anomaly detection model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train model on 214587 examples\n",
+      "Model trained in 0:00:00.074241\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = ydf.IsolationForestLearner(features=features).train(filtered_dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then generate \"predictions\" i.e. anomaly scores."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.57844853, 0.609949  , 0.5433627 , 0.6099571 , 0.48067462],\n",
+       "      dtype=float32)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predictions = model.predict(filtered_dataset)\n",
+    "predictions[:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we plot the model anomaly score's distribution for spruce/fir and cottonwood/willow cover. We se than both distributions are \"separated\", indicating the model's ability to differentiate between the two covers.\n",
+    "\n",
+    "**Note:** It's important to note that since cottonwood/willow cover is less frequent, the two distributions are normalized separately. Otherwise, the cottonwood/willow distribution would appear flat."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.kdeplot(predictions[filtered_dataset[\"Cover_Type\"] == \"Spruce/Fir\"], label=\"Spruce/Fir\")\n",
+    "sns.kdeplot(predictions[filtered_dataset[\"Cover_Type\"] == \"Cottonwood/Willow\"], label=\"Cottonwood/Willow\")\n",
+    "plt.xlabel(\"predicted anomaly score\")\n",
+    "plt.ylabel(\"distribution\")\n",
+    "plt.legend()\n",
+    "None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The AUC is a metric used to evaluate classification models. It can also be used to quantify the discriminative power of any signal in separating two distinct classes. In the context of anomaly detection, we can use the AUC to quantify how much our anomaly detection model is able to isolate the minority class.\n",
+    "\n",
+    "The cover type information are not used to train the model and the dataset is considered static (i.e., the type of coverage does not change overtime). Therefore, we do not need to split the dataset between training and testing, and use all the data both for training the model and evaluate it with the AUC."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9427246186652949"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics.roc_auc_score(filtered_dataset[\"Cover_Type\"] == \"Cottonwood/Willow\", predictions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This high AUC confirms that the model is well able to separate the two cover types.\n",
+    "\n",
+    "We can also analyse the model to understand it: For instance, we see on the partial dependency plot of the elevation that the \"normal\" coverage is around 2900 and 3300 meters of altitude. Other similar conclusions can be taken by looking at other attributes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
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+       "      title: 'Horizontal_Distance_To_Fire_Points',\n",
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+       "</div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "<ydf.model.analysis.Analysis at 0x7faabecce4d0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.analyze(filtered_dataset, sampling=0.001) # Use larger sampling for better results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also interpret individual model predictions. For example, let's select the first Cottonwood/Willow example and generate a prediction:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
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+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Elevation</th>\n",
+       "      <th>Aspect</th>\n",
+       "      <th>Slope</th>\n",
+       "      <th>Horizontal_Distance_To_Hydrology</th>\n",
+       "      <th>Vertical_Distance_To_Hydrology</th>\n",
+       "      <th>Horizontal_Distance_To_Roadways</th>\n",
+       "      <th>Hillshade_9am</th>\n",
+       "      <th>Hillshade_Noon</th>\n",
+       "      <th>Hillshade_3pm</th>\n",
+       "      <th>Horizontal_Distance_To_Fire_Points</th>\n",
+       "      <th>Cover_Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1988</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>318</td>\n",
+       "      <td>7</td>\n",
+       "      <td>30</td>\n",
+       "      <td>4</td>\n",
+       "      <td>108</td>\n",
+       "      <td>201</td>\n",
+       "      <td>234</td>\n",
+       "      <td>172</td>\n",
+       "      <td>268</td>\n",
+       "      <td>Cottonwood/Willow</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Elevation  Aspect  Slope  Horizontal_Distance_To_Hydrology  \\\n",
+       "1988       2000     318      7                                30   \n",
+       "\n",
+       "      Vertical_Distance_To_Hydrology  Horizontal_Distance_To_Roadways  \\\n",
+       "1988                               4                              108   \n",
+       "\n",
+       "      Hillshade_9am  Hillshade_Noon  Hillshade_3pm  \\\n",
+       "1988            201             234            172   \n",
+       "\n",
+       "      Horizontal_Distance_To_Fire_Points         Cover_Type  \n",
+       "1988                                 268  Cottonwood/Willow  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "first_willow_example = filtered_dataset[filtered_dataset[\"Cover_Type\"] == \"Cottonwood/Willow\"][:1]\n",
+    "first_willow_example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.5474113], dtype=float32)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.predict(first_willow_example)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's see how the model prediction would change with the feature values of this example:\n",
+    "\n",
+    "We see than the example elevation of 2000 is uncommon and explain some of the high prediction value. On the other hand, the example \"aspect\" and \"slope\" are relatively normal."
+   ]
+  },
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+       "<script>\n",
+       "  Plotly.newPlot(\n",
+       "    'chart_5188_8546_c36f_20a7_feature_variation_0',\n",
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+       "x: [1988,2022.65,2057.31,2091.96,2126.61,2161.27,2195.92,2230.57,2265.22,2299.88,2334.53,2369.18,2403.84,2438.49,2473.14,2507.8,2542.45,2577.1,2611.76,2646.41,2681.06,2715.71,2750.37,2785.02,2819.67,2854.33,2888.98,2923.63,2958.29,2992.94,3027.59,3062.24,3096.9,3131.55,3166.2,3200.86,3235.51,3270.16,3304.82,3339.47,3374.12,3408.78,3443.43,3478.08,3512.73,3547.39,3582.04,3616.69,3651.35,3686],\n",
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+       "x: [0,145.265,290.531,435.796,581.061,726.327,871.592,1016.86,1162.12,1307.39,1452.65,1597.92,1743.18,1888.45,2033.71,2178.98,2324.24,2469.51,2614.78,2760.04,2905.31,3050.57,3195.84,3341.1,3486.37,3631.63,3776.9,3922.16,4067.43,4212.69,4357.96,4503.22,4648.49,4793.75,4939.02,5084.29,5229.55,5374.82,5520.08,5665.35,5810.61,5955.88,6101.14,6246.41,6391.67,6536.94,6682.2,6827.47,6972.73,7118],\n",
+       "y: [0.552863,0.552863,0.547053,0.541576,0.538269,0.536741,0.53466,0.53395,0.533858,0.534641,0.534627,0.535806,0.536031,0.538364,0.538115,0.539286,0.539415,0.541832,0.544421,0.549287,0.550695,0.553135,0.554703,0.559278,0.561248,0.563868,0.565624,0.568023,0.571468,0.573777,0.575474,0.577287,0.580254,0.580407,0.58388,0.586304,0.586413,0.590936,0.591556,0.594765,0.598193,0.600421,0.603101,0.607091,0.607091,0.608801,0.611544,0.611812,0.61208,0.61208],\n",
+       "type: 'scatter',\n",
+       "mode: 'lines',\n",
+       "line: {\n",
+       "  dash: 'solid',\n",
+       "  width: 1\n",
+       "},\n",
+       "name: 'output',\n",
+       "},\n",
+       "{\n",
+       "x: [268,268],\n",
+       "y: [0.533858,0.61208],\n",
+       "type: 'scatter',\n",
+       "mode: 'lines',\n",
+       "line: {\n",
+       "  dash: 'dot',\n",
+       "  width: 1\n",
+       "},\n",
+       "name: 'selected',\n",
+       "},\n",
+       "],\n",
+       "    {\n",
+       "      width: 400,\n",
+       "      height: 300,\n",
+       "      title: '',\n",
+       "      showlegend: true,\n",
+       "      xaxis: {\n",
+       "        ticks: 'outside',\n",
+       "        showgrid: true,\n",
+       "        zeroline: false,\n",
+       "        showline: true,\n",
+       "        title: 'Horizontal_Distance_To_Fire_Points',\n",
+       "        },\n",
+       "      font: {\n",
+       "        size: 10,\n",
+       "        },\n",
+       "      yaxis: {\n",
+       "        ticks: 'outside',\n",
+       "        showgrid: true,\n",
+       "        zeroline: false,\n",
+       "        showline: true,\n",
+       "        title: 'prediction',\n",
+       "        },\n",
+       "      margin: {\n",
+       "        l: 50,\n",
+       "        r: 50,\n",
+       "        b: 50,\n",
+       "        t: 50,\n",
+       "      },\n",
+       "    },\n",
+       "    {\n",
+       "      modeBarButtonsToRemove: ['sendDataToCloud'],\n",
+       "      displaylogo: false,displayModeBar: false,\n",
+       "    }\n",
+       "  );\n",
+       "</script>\n",
+       "</div></div></div>"
+      ],
+      "text/plain": [
+       "<ydf.model.analysis.PredictionAnalysis at 0x7faabed02710>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.analyze_prediction(first_willow_example)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "List all the decision forest algorithms, our isolation forest model define an implicit distance between examples. This distance can be use to cluster the examples or interpretable mapping.\n",
+    "\n",
+    "Let's compute the distance between each pair of examples. To make the code run fast, we only select the first 10000 examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.       , 0.86     , 0.6766667, 0.85     ],\n",
+       "       [0.86     , 0.       , 0.9066667, 0.31     ],\n",
+       "       [0.6766667, 0.9066667, 0.       , 0.8833333],\n",
+       "       [0.85     , 0.31     , 0.8833333, 0.       ]], dtype=float32)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "distances = model.distance(filtered_dataset[:10000]) # Use more examples for better results\n",
+    "distances[:4, :4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then use UMAP (or any other manifold learning algorithm such as T-SNE) to project the examples in a 2D plot.\n",
+    "\n",
+    "Note that the cover types are well separated despite the model having never seen them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/google/home/gbm/my_venv/lib/python3.11/site-packages/umap/umap_.py:1858: UserWarning:\n",
+      "\n",
+      "using precomputed metric; inverse_transform will be unavailable\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7faa84413490>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "manifold = umap.UMAP(n_components=2, n_neighbors=10, metric=\"precomputed\").fit_transform(distances)\n",
+    "sns.scatterplot(x=manifold[:, 0],\n",
+    "                y=manifold[:, 1],\n",
+    "                hue=filtered_dataset[\"Cover_Type\"][:manifold.shape[0]])\n",
+    "plt.legend()"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "private_outputs": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/documentation/public/mkdocs.yml b/documentation/public/mkdocs.yml
index 18128006..9c1b2144 100644
--- a/documentation/public/mkdocs.yml
+++ b/documentation/public/mkdocs.yml
@@ -56,6 +56,7 @@ nav:
     - Regression: tutorial/regression.ipynb
     - Ranking: tutorial/ranking.ipynb
     - Uplifting: tutorial/uplifting.ipynb
+    - Anomaly detection: tutorial/anomaly_detection.ipynb
   - Input feature:
     - numerical: tutorial/numerical_feature.ipynb
     - categorical: tutorial/categorical_feature.ipynb