diff --git a/lab_4/lab_4.ipynb b/lab_4/lab_4.ipynb
index b1c86bc..d9069a0 100644
--- a/lab_4/lab_4.ipynb
+++ b/lab_4/lab_4.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 313,
+   "execution_count": 337,
    "metadata": {},
    "outputs": [
     {
@@ -375,7 +375,7 @@
        "[10000 rows x 21 columns]"
       ]
      },
-     "execution_count": 313,
+     "execution_count": 337,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -401,7 +401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 314,
+   "execution_count": 338,
    "metadata": {},
    "outputs": [
     {
@@ -1026,7 +1026,7 @@
        "[20 rows x 22 columns]"
       ]
      },
-     "execution_count": 314,
+     "execution_count": 338,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1062,7 +1062,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 315,
+   "execution_count": 339,
    "metadata": {},
    "outputs": [
     {
@@ -2110,7 +2110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 316,
+   "execution_count": 340,
    "metadata": {},
    "outputs": [
     {
@@ -2713,7 +2713,7 @@
        "[20 rows x 22 columns]"
       ]
      },
-     "execution_count": 316,
+     "execution_count": 340,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2799,6 +2799,7 @@
     "    ]\n",
     "\n",
     ")\n",
+    "# preprocessing_result = pipeline_end.fit_transform(X_train.values)\n",
     "cols = ['price_h', 'price_l', 'price_m', 'price_vh']\n",
     "preprocessing_result = features_preprocessing.fit_transform(X_train)\n",
     "preprocessing_result = pd.DataFrame(preprocessing_result, columns=num_columns + cat_columns + cols + columns_to_drop)\n",
@@ -2834,7 +2835,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 317,
+   "execution_count": 341,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2877,7 +2878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 320,
+   "execution_count": 343,
    "metadata": {},
    "outputs": [
     {
@@ -2898,7 +2899,7 @@
       "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'columns'",
       "\nDuring handling of the above exception, another exception occurred:\n",
       "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[320], line 9\u001b[0m\n\u001b[0;32m      6\u001b[0m model \u001b[38;5;241m=\u001b[39m class_models[model_name][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m      8\u001b[0m model_pipeline \u001b[38;5;241m=\u001b[39m Pipeline([(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpipeline\u001b[39m\u001b[38;5;124m\"\u001b[39m, pipeline_end), (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m, model)])\n\u001b[1;32m----> 9\u001b[0m model_pipeline \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_pipeline\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mravel\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     11\u001b[0m y_train_predict \u001b[38;5;241m=\u001b[39m model_pipeline\u001b[38;5;241m.\u001b[39mpredict(X_train)\n\u001b[0;32m     12\u001b[0m y_test_probs \u001b[38;5;241m=\u001b[39m model_pipeline\u001b[38;5;241m.\u001b[39mpredict_proba(X_test)[:, \u001b[38;5;241m1\u001b[39m]\n",
+      "Cell \u001b[1;32mIn[343], line 9\u001b[0m\n\u001b[0;32m      6\u001b[0m model \u001b[38;5;241m=\u001b[39m class_models[model_name][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m      8\u001b[0m model_pipeline \u001b[38;5;241m=\u001b[39m Pipeline([(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpipeline\u001b[39m\u001b[38;5;124m\"\u001b[39m, pipeline_end), (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m, model)])\n\u001b[1;32m----> 9\u001b[0m model_pipeline \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_pipeline\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mravel\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     11\u001b[0m y_train_predict \u001b[38;5;241m=\u001b[39m model_pipeline\u001b[38;5;241m.\u001b[39mpredict(X_train)\n\u001b[0;32m     12\u001b[0m y_test_probs \u001b[38;5;241m=\u001b[39m model_pipeline\u001b[38;5;241m.\u001b[39mpredict_proba(X_test)[:, \u001b[38;5;241m1\u001b[39m]\n",
       "File \u001b[1;32md:\\Study\\3 курс 5 семестр\\AIM\\AIM-PIbd-31-Yakovlev-M-G\\kernel\\Lib\\site-packages\\sklearn\\base.py:1473\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1466\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m   1468\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m   1469\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m   1470\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m   1471\u001b[0m     )\n\u001b[0;32m   1472\u001b[0m ):\n\u001b[1;32m-> 1473\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[1;32md:\\Study\\3 курс 5 семестр\\AIM\\AIM-PIbd-31-Yakovlev-M-G\\kernel\\Lib\\site-packages\\sklearn\\pipeline.py:469\u001b[0m, in \u001b[0;36mPipeline.fit\u001b[1;34m(self, X, y, **params)\u001b[0m\n\u001b[0;32m    426\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Fit the model.\u001b[39;00m\n\u001b[0;32m    427\u001b[0m \n\u001b[0;32m    428\u001b[0m \u001b[38;5;124;03mFit all the transformers one after the other and sequentially transform the\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    466\u001b[0m \u001b[38;5;124;03m    Pipeline with fitted steps.\u001b[39;00m\n\u001b[0;32m    467\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    468\u001b[0m routed_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_method_params(method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfit\u001b[39m\u001b[38;5;124m\"\u001b[39m, props\u001b[38;5;241m=\u001b[39mparams)\n\u001b[1;32m--> 469\u001b[0m Xt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrouted_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    470\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _print_elapsed_time(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPipeline\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_log_message(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)):\n\u001b[0;32m    471\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_final_estimator \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpassthrough\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
       "File \u001b[1;32md:\\Study\\3 курс 5 семестр\\AIM\\AIM-PIbd-31-Yakovlev-M-G\\kernel\\Lib\\site-packages\\sklearn\\pipeline.py:406\u001b[0m, in \u001b[0;36mPipeline._fit\u001b[1;34m(self, X, y, routed_params)\u001b[0m\n\u001b[0;32m    404\u001b[0m     cloned_transformer \u001b[38;5;241m=\u001b[39m clone(transformer)\n\u001b[0;32m    405\u001b[0m \u001b[38;5;66;03m# Fit or load from cache the current transformer\u001b[39;00m\n\u001b[1;32m--> 406\u001b[0m X, fitted_transformer \u001b[38;5;241m=\u001b[39m \u001b[43mfit_transform_one_cached\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    407\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcloned_transformer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    408\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    409\u001b[0m \u001b[43m    \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    410\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    411\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmessage_clsname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPipeline\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    412\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmessage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_log_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep_idx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    413\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrouted_params\u001b[49m\u001b[43m[\u001b[49m\u001b[43mname\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    414\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    415\u001b[0m \u001b[38;5;66;03m# Replace the transformer of the step with the fitted\u001b[39;00m\n\u001b[0;32m    416\u001b[0m \u001b[38;5;66;03m# transformer. This is necessary when loading the transformer\u001b[39;00m\n\u001b[0;32m    417\u001b[0m \u001b[38;5;66;03m# from the cache.\u001b[39;00m\n\u001b[0;32m    418\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps[step_idx] \u001b[38;5;241m=\u001b[39m (name, fitted_transformer)\n",
@@ -2927,7 +2928,7 @@
     "    model = class_models[model_name][\"model\"]\n",
     "\n",
     "    model_pipeline = Pipeline([(\"pipeline\", pipeline_end), (\"model\", model)])\n",
-    "    model_pipeline = model_pipeline.fit(X_train, y_train.values.ravel())\n",
+    "    model_pipeline = model_pipeline.fit(X_train.values, y_train.values.ravel())\n",
     "\n",
     "    y_train_predict = model_pipeline.predict(X_train)\n",
     "    y_test_probs = model_pipeline.predict_proba(X_test)[:, 1]\n",