regression-ensembles.ipynb

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   "source": [
    "# Регрессия: ближайшие соседи"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor, ExtraTreesRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error, mean_absolute_percentage_error # метрики подгонки\n",
    "from sklearn.metrics import mean_squared_log_error, median_absolute_error, explained_variance_score # метрики подгонки\n",
    "\n",
    "# Не показывать Warning\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=Warning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce5f22de-f107-45a3-b1a2-0ed26be57eb4",
   "metadata": {},
   "source": [
    "## Данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b6ac49c-4b49-4ca7-a0c4-180073205922",
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   "outputs": [],
   "source": [
    "# загрузим данные\n",
    "df = pd.read_csv('./datasets/sleep75.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c6a2fc6-cf8b-48ee-983c-5d51b5c21c26",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['sleep']\n",
    "X = df[['totwrk', 'age', 'educ', 'male', 'south', 'smsa']]\n",
    "# Разобьём выборку на обучающую и тестовую 80:20\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e20a099",
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   "source": [
    "rgr_boost = GradientBoostingRegressor()\n",
    "rgr_boost.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cceafda",
   "metadata": {},
   "outputs": [],
   "source": [
    "rgr_forest = RandomForestRegressor()\n",
    "rgr_forest.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51280be5",
   "metadata": {},
   "outputs": [],
   "source": [
    "rgr_extratrees = ExtraTreesRegressor()\n",
    "rgr_extratrees.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db2cf637-11d7-4e9b-955f-383e2252f642",
   "metadata": {},
   "source": [
    "## Метрики подгонки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3744ae2b-7671-4a5b-9f67-407c90e1e922",
   "metadata": {},
   "outputs": [],
   "source": [
    "# R^2 на тестовой выборке\n",
    "rgr_boost.score(X_test, y_test), rgr_forest.score(X_test, y_test), rgr_extratrees.score(X_test, y_test)"
   ]
  }
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