diff --git "a/src/test.ipynb" "b/src/test.ipynb"
--- "a/src/test.ipynb"
+++ "b/src/test.ipynb"
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 9,
    "metadata": {
     "collapsed": true,
     "pycharm": {
@@ -11,37 +11,35 @@
    },
    "outputs": [],
    "source": [
+    "from typing import Any\n",
     "import pandas as pd\n",
     "from sklearn.model_selection import train_test_split\n",
-    "from sklift.datasets import fetch_lenta\n",
+    "from sklift.datasets import fetch_hillstrom\n",
     "from sklift.metrics import uplift_at_k\n",
     "from sklift.viz import plot_uplift_preds\n",
     "from sklift.models import SoloModel\n",
-    "from catboost import CatBoostClassifier"
+    "from catboost import CatBoostClassifier\n",
+    "from category_encoders import CatBoostEncoder"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset type: <class 'sklearn.utils._bunch.Bunch'>\n",
-      "\n",
-      "Dataset features shape: (687029, 193)\n",
-      "Dataset target shape: (687029,)\n",
-      "Dataset treatment shape: (687029,)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "dataset = fetch_lenta()\n",
-    "print(f\"Dataset type: {type(dataset)}\\n\")\n",
-    "print(f\"Dataset features shape: {dataset.data.shape}\")\n",
-    "print(f\"Dataset target shape: {dataset.target.shape}\")\n",
-    "print(f\"Dataset treatment shape: {dataset.treatment.shape}\")"
+    "def get_data() -> tuple[Any, Any, Any]:\n",
+    "\t# получаем датасет\n",
+    "\tdataset = fetch_hillstrom(target_col='visit')\n",
+    "\tdataset, target, treatment = dataset['data'], dataset['target'], dataset['treatment']\n",
+    "\t# выбираем два сегмента\n",
+    "\tdataset = dataset[treatment != 'Mens E-Mail']\n",
+    "\ttarget = target[treatment != 'Mens E-Mail']\n",
+    "\ttreatment = treatment[treatment != 'Mens E-Mail'].map({\n",
+    "\t\t'Womens E-Mail': 1,\n",
+    "\t\t'No E-Mail':     0\n",
+    "\t})\n",
+    "\n",
+    "\treturn dataset, target, treatment"
    ],
    "metadata": {
     "collapsed": false,
@@ -52,10 +50,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "outputs": [],
    "source": [
-    "norm_columns = ['age', 'children', 'gender', 'main_format', 'months_from_register', 'response_sms', 'response_viber']"
+    "data, target, treatment = get_data()"
    ],
    "metadata": {
     "collapsed": false,
@@ -66,20 +64,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "outputs": [
     {
      "data": {
-      "text/plain": "    age  children gender  main_format  months_from_register  response_sms  \\\n0  47.0       0.0      Ж            0                  18.0      0.923077   \n1  57.0       0.0      Ж            1                   4.0      1.000000   \n2  38.0       3.0      М            0                  34.0      1.000000   \n3  65.0       0.0      Ж            1                  40.0      0.909091   \n4  61.0       2.0      Ж            0                  20.0      1.000000   \n\n   response_viber  \n0        0.071429  \n1        0.000000  \n2        0.250000  \n3        0.000000  \n4        0.100000  ",
-      "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>age</th>\n      <th>children</th>\n      <th>gender</th>\n      <th>main_format</th>\n      <th>months_from_register</th>\n      <th>response_sms</th>\n      <th>response_viber</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>47.0</td>\n      <td>0.0</td>\n      <td>Ж</td>\n      <td>0</td>\n      <td>18.0</td>\n      <td>0.923077</td>\n      <td>0.071429</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>57.0</td>\n      <td>0.0</td>\n      <td>Ж</td>\n      <td>1</td>\n      <td>4.0</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>38.0</td>\n      <td>3.0</td>\n      <td>М</td>\n      <td>0</td>\n      <td>34.0</td>\n      <td>1.000000</td>\n      <td>0.250000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>65.0</td>\n      <td>0.0</td>\n      <td>Ж</td>\n      <td>1</td>\n      <td>40.0</td>\n      <td>0.909091</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>61.0</td>\n      <td>2.0</td>\n      <td>Ж</td>\n      <td>0</td>\n      <td>20.0</td>\n      <td>1.000000</td>\n      <td>0.100000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/plain": "   recency history_segment  history  mens  womens   zip_code  newbie channel\n0       10  2) $100 - $200   142.44     1       0  Surburban       0   Phone\n1        6  3) $200 - $350   329.08     1       1      Rural       1     Web\n2        7  2) $100 - $200   180.65     0       1  Surburban       1     Web\n4        2    1) $0 - $100    45.34     1       0      Urban       0     Web\n5        6  2) $100 - $200   134.83     0       1  Surburban       0   Phone",
+      "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>recency</th>\n      <th>history_segment</th>\n      <th>history</th>\n      <th>mens</th>\n      <th>womens</th>\n      <th>zip_code</th>\n      <th>newbie</th>\n      <th>channel</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10</td>\n      <td>2) $100 - $200</td>\n      <td>142.44</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Surburban</td>\n      <td>0</td>\n      <td>Phone</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>6</td>\n      <td>3) $200 - $350</td>\n      <td>329.08</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Rural</td>\n      <td>1</td>\n      <td>Web</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7</td>\n      <td>2) $100 - $200</td>\n      <td>180.65</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Surburban</td>\n      <td>1</td>\n      <td>Web</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>1) $0 - $100</td>\n      <td>45.34</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Urban</td>\n      <td>0</td>\n      <td>Web</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>2) $100 - $200</td>\n      <td>134.83</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Surburban</td>\n      <td>0</td>\n      <td>Phone</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 10,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dataset.data[norm_columns].head()"
+    "data.head()"
    ],
    "metadata": {
     "collapsed": false,
@@ -90,38 +88,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 5,
    "outputs": [
     {
      "data": {
-      "text/plain": "0.0    416983\n1.0    126636\n2.0    109892\n3.0     24257\n4.0       530\n5.0        91\n6.0        46\n7.0         9\n9.0         2\n8.0         2\nName: children, dtype: int64"
+      "text/plain": "0    0\n1    0\n2    0\n4    0\n5    1\nName: visit, dtype: int64"
      },
-     "execution_count": 17,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dataset.data['children'].value_counts()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "outputs": [],
-   "source": [
-    "treat_dict = {\n",
-    "    'test': 1,\n",
-    "    'control': 0\n",
-    "}\n",
-    "\n",
-    "dataset.treatment = dataset.treatment.map(treat_dict)"
+    "target.head()"
    ],
    "metadata": {
     "collapsed": false,
@@ -132,23 +111,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Ж               433448\n",
-      "М               243910\n",
-      "Не определен      9671\n",
-      "Name: gender, dtype: int64\n"
-     ]
+     "data": {
+      "text/plain": "0    1\n1    0\n2    1\n4    1\n5    1\nName: segment, dtype: int64"
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "dataset.data['gender'] = dataset.data['gender'].fillna(value='Не определен')\n",
-    "\n",
-    "print(dataset.data['gender'].value_counts(dropna=False))"
+    "treatment.head()"
    ],
    "metadata": {
     "collapsed": false,
@@ -159,31 +134,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 23,
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train shape: (480920, 193)\n",
-      "Validation shape: (206109, 193)\n"
-     ]
+     "data": {
+      "text/plain": "visit           0         1\nsegment                    \n0        0.893833  0.106167\n1        0.848600  0.151400",
+      "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>visit</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n    <tr>\n      <th>segment</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.893833</td>\n      <td>0.106167</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.848600</td>\n      <td>0.151400</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "stratify_cols = pd.concat([dataset.treatment, dataset.target], axis=1)\n",
-    "\n",
-    "X_train, X_val, trmnt_train, trmnt_val, y_train, y_val = train_test_split(\n",
-    "    dataset.data,\n",
-    "    dataset.treatment,\n",
-    "    dataset.target,\n",
-    "    stratify=stratify_cols,\n",
-    "    test_size=0.3,\n",
-    "    random_state=42\n",
-    ")\n",
-    "\n",
-    "print(f\"Train shape: {X_train.shape}\")\n",
-    "print(f\"Validation shape: {X_val.shape}\")"
+    "pd.crosstab(treatment, target, normalize='index')"
    ],
    "metadata": {
     "collapsed": false,
@@ -194,20 +158,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 35,
    "outputs": [
     {
      "data": {
-      "text/plain": "         age  cheque_count_12m_g20  cheque_count_12m_g21  \\\n89820   36.0                   2.0                   3.0   \n539582  34.0                  16.0                  52.0   \n237165  39.0                   0.0                   0.0   \n653847  26.0                   3.0                   4.0   \n94205   46.0                   0.0                   7.0   \n\n        cheque_count_12m_g25  cheque_count_12m_g32  cheque_count_12m_g33  \\\n89820                    1.0                   1.0                  12.0   \n539582                  17.0                  66.0                  59.0   \n237165                   3.0                   1.0                   1.0   \n653847                   5.0                   0.0                   3.0   \n94205                   11.0                   5.0                   1.0   \n\n        cheque_count_12m_g38  cheque_count_12m_g39  cheque_count_12m_g41  \\\n89820                   13.0                   2.0                   2.0   \n539582                  30.0                   9.0                  32.0   \n237165                   0.0                   1.0                   1.0   \n653847                   3.0                   1.0                   2.0   \n94205                    6.0                   0.0                   6.0   \n\n        cheque_count_12m_g42  ...  sale_sum_6m_g24  sale_sum_6m_g25  \\\n89820                   18.0  ...           932.78             0.00   \n539582                  53.0  ...          3308.32            82.07   \n237165                   1.0  ...           105.79           214.92   \n653847                   3.0  ...             0.00             0.00   \n94205                    0.0  ...          1513.76          1618.35   \n\n        sale_sum_6m_g26  sale_sum_6m_g32  sale_sum_6m_g33  sale_sum_6m_g44  \\\n89820             47.88             0.00           145.87           470.92   \n539582          1541.52          1248.53           633.48          1841.38   \n237165            22.99            39.98           172.99           139.64   \n653847             0.00             0.00             0.00             0.00   \n94205            233.89           471.20           394.21          1384.44   \n\n        sale_sum_6m_g54  stdev_days_between_visits_15d  \\\n89820             38.99                            0.0   \n539582           186.73                            0.0   \n237165            69.57                            0.0   \n653847            86.52                            0.0   \n94205            402.20                            0.0   \n\n        stdev_discount_depth_15d  stdev_discount_depth_1m  \n89820                        NaN                   0.1969  \n539582                       NaN                   0.2559  \n237165                       0.0                      NaN  \n653847                       0.0                   0.0000  \n94205                        0.0                      NaN  \n\n[5 rows x 193 columns]",
-      "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>age</th>\n      <th>cheque_count_12m_g20</th>\n      <th>cheque_count_12m_g21</th>\n      <th>cheque_count_12m_g25</th>\n      <th>cheque_count_12m_g32</th>\n      <th>cheque_count_12m_g33</th>\n      <th>cheque_count_12m_g38</th>\n      <th>cheque_count_12m_g39</th>\n      <th>cheque_count_12m_g41</th>\n      <th>cheque_count_12m_g42</th>\n      <th>...</th>\n      <th>sale_sum_6m_g24</th>\n      <th>sale_sum_6m_g25</th>\n      <th>sale_sum_6m_g26</th>\n      <th>sale_sum_6m_g32</th>\n      <th>sale_sum_6m_g33</th>\n      <th>sale_sum_6m_g44</th>\n      <th>sale_sum_6m_g54</th>\n      <th>stdev_days_between_visits_15d</th>\n      <th>stdev_discount_depth_15d</th>\n      <th>stdev_discount_depth_1m</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>89820</th>\n      <td>36.0</td>\n      <td>2.0</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>12.0</td>\n      <td>13.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>18.0</td>\n      <td>...</td>\n      <td>932.78</td>\n      <td>0.00</td>\n      <td>47.88</td>\n      <td>0.00</td>\n      <td>145.87</td>\n      <td>470.92</td>\n      <td>38.99</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.1969</td>\n    </tr>\n    <tr>\n      <th>539582</th>\n      <td>34.0</td>\n      <td>16.0</td>\n      <td>52.0</td>\n      <td>17.0</td>\n      <td>66.0</td>\n      <td>59.0</td>\n      <td>30.0</td>\n      <td>9.0</td>\n      <td>32.0</td>\n      <td>53.0</td>\n      <td>...</td>\n      <td>3308.32</td>\n      <td>82.07</td>\n      <td>1541.52</td>\n      <td>1248.53</td>\n      <td>633.48</td>\n      <td>1841.38</td>\n      <td>186.73</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.2559</td>\n    </tr>\n    <tr>\n      <th>237165</th>\n      <td>39.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>105.79</td>\n      <td>214.92</td>\n      <td>22.99</td>\n      <td>39.98</td>\n      <td>172.99</td>\n      <td>139.64</td>\n      <td>69.57</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>653847</th>\n      <td>26.0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>3.0</td>\n      <td>...</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>86.52</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0000</td>\n    </tr>\n    <tr>\n      <th>94205</th>\n      <td>46.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>11.0</td>\n      <td>5.0</td>\n      <td>1.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1513.76</td>\n      <td>1618.35</td>\n      <td>233.89</td>\n      <td>471.20</td>\n      <td>394.21</td>\n      <td>1384.44</td>\n      <td>402.20</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 193 columns</p>\n</div>"
+      "text/plain": "'Urban'"
      },
-     "execution_count": 24,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "40.048719930667794"
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "X_val.head()"
+    "res = data.zip_code.value_counts(normalize=True) * 100\n",
+    "display(res.index[1], res[1])"
    ],
    "metadata": {
     "collapsed": false,
@@ -218,9 +188,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 12,
    "outputs": [],
    "source": [
+    "X_train, X_val, y_train, y_val, trmnt_train, trmnt_val = train_test_split(\n",
+    "    data, target, treatment, test_size=0.3, random_state=42\n",
+    ")\n",
+    "\n",
     "models_results = {\n",
     "    'approach': [],\n",
     "    'uplift@30%': []\n",
@@ -247,12 +221,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 13,
    "outputs": [
     {
      "data": {
       "text/plain": "<Figure size 1440x504 with 3 Axes>",
-      "image/png": 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\n"
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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -262,7 +236,7 @@
    ],
    "source": [
     "sm = SoloModel(CatBoostClassifier(iterations=1000, task_type=\"GPU\", random_state=42, silent=True))\n",
-    "sm = sm.fit(X_train, y_train, trmnt_train, estimator_fit_params={'cat_features': ['gender']})\n",
+    "sm = sm.fit(X_train, y_train, trmnt_train, estimator_fit_params={'cat_features': ['history_segment', 'zip_code', 'channel']})\n",
     "\n",
     "uplift_sm = sm.predict(X_val)\n",
     "\n",
@@ -288,14 +262,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 14,
    "outputs": [
     {
      "data": {
-      "text/plain": "                            feature_name  feature_score\n0           k_var_days_between_visits_3m      10.261643\n1          k_var_days_between_visits_15d       5.330679\n2                          food_share_1m       4.544786\n3                   cheque_count_12m_g48       4.329268\n4    perdelta_days_between_visits_15_30d       4.319264\n..                                   ...            ...\n189        k_var_count_per_cheque_6m_g32       0.023904\n190                     sale_sum_12m_g54       0.023079\n191                    sale_count_6m_g44       0.017349\n192                    sale_count_6m_g57       0.009368\n193                               gender       0.000000\n\n[194 rows x 2 columns]",
-      "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>feature_name</th>\n      <th>feature_score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>k_var_days_between_visits_3m</td>\n      <td>10.261643</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>k_var_days_between_visits_15d</td>\n      <td>5.330679</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>food_share_1m</td>\n      <td>4.544786</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>cheque_count_12m_g48</td>\n      <td>4.329268</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>perdelta_days_between_visits_15_30d</td>\n      <td>4.319264</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>189</th>\n      <td>k_var_count_per_cheque_6m_g32</td>\n      <td>0.023904</td>\n    </tr>\n    <tr>\n      <th>190</th>\n      <td>sale_sum_12m_g54</td>\n      <td>0.023079</td>\n    </tr>\n    <tr>\n      <th>191</th>\n      <td>sale_count_6m_g44</td>\n      <td>0.017349</td>\n    </tr>\n    <tr>\n      <th>192</th>\n      <td>sale_count_6m_g57</td>\n      <td>0.009368</td>\n    </tr>\n    <tr>\n      <th>193</th>\n      <td>gender</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>194 rows × 2 columns</p>\n</div>"
+      "text/plain": "      feature_name  feature_score\n0           newbie      16.159375\n1          history      15.380477\n2          recency      15.057106\n3           womens      12.918455\n4        treatment      12.083391\n5          channel       9.437116\n6         zip_code       9.357430\n7             mens       6.807001\n8  history_segment       2.799648",
+      "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>feature_name</th>\n      <th>feature_score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>newbie</td>\n      <td>16.159375</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>history</td>\n      <td>15.380477</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>recency</td>\n      <td>15.057106</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>womens</td>\n      <td>12.918455</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>treatment</td>\n      <td>12.083391</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>channel</td>\n      <td>9.437116</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>zip_code</td>\n      <td>9.357430</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>mens</td>\n      <td>6.807001</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>history_segment</td>\n      <td>2.799648</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 31,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -329,14 +303,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 15,
    "outputs": [],
    "source": [
     "from sklift.models import ClassTransformation\n",
     "\n",
     "\n",
     "ct = ClassTransformation(CatBoostClassifier(iterations=1000, task_type='GPU', random_state=42, silent=True))\n",
-    "ct = ct.fit(X_train, y_train, trmnt_train, estimator_fit_params={'cat_features': ['gender']})\n",
+    "ct = ct.fit(X_train, y_train, trmnt_train, estimator_fit_params={'cat_features': ['history_segment', 'zip_code', 'channel']})\n",
     "\n",
     "uplift_ct = ct.predict(X_val)\n",
     "\n",
@@ -366,12 +340,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 16,
    "outputs": [
     {
      "data": {
       "text/plain": "<Figure size 1440x504 with 3 Axes>",
-      "image/png": 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\n"
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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -390,8 +364,8 @@
     ")\n",
     "tm = tm.fit(\n",
     "    X_train, y_train, trmnt_train,\n",
-    "    estimator_trmnt_fit_params={'cat_features': ['gender']},\n",
-    "    estimator_ctrl_fit_params={'cat_features': ['gender']}\n",
+    "    estimator_trmnt_fit_params={'cat_features': ['history_segment', 'zip_code', 'channel']},\n",
+    "    estimator_ctrl_fit_params={'cat_features': ['history_segment', 'zip_code', 'channel']}\n",
     ")\n",
     "\n",
     "uplift_tm = tm.predict(X_val)\n",