cross-val-scores.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import validation_curve, cross_val_score\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "df = pd.read_csv('./datasets/cholesterol_prediction.csv',sep=\";\")\n",
    "df.head()"
   ]
  },
  {
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   "source": [
    "y = df['cardio']\n",
    "X = df.drop(columns=['id','cardio'])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2)"
   ]
  },
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   "source": [
    "# результат k-Fold валидации на обучающей выборке модели с заданными параметрами\n",
    "rf = RandomForestClassifier(n_estimators=10, criterion='gini', max_depth=None, random_state=42)\n",
    "\n",
    "scores = cross_val_score(rf, X_train, y_train, cv=5, scoring=\"accuracy\")\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Данные для Validation Curve\n",
    "rf = RandomForestClassifier(n_estimators=100, criterion='gini', max_depth=None, random_state=42)\n",
    "\n",
    "param_range = np.arange(10, 101, 10)\n",
    "\n",
    "train_scores, test_scores = validation_curve(rf, X_train, y_train, param_name=\"n_estimators\", param_range=param_range, cv=5, scoring=\"accuracy\")\n",
    "train_scores, test_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Данные для Validation Curve\n",
    "rf = RandomForestClassifier(n_estimators=100, criterion='gini', max_depth=None, random_state=42)\n",
    "\n",
    "param_range = np.arange(10, 101, 10)\n",
    "\n",
    "train_scores, test_scores = validation_curve(rf, X_train, y_train, param_name=\"n_estimators\", param_range=param_range, cv=5, scoring=\"roc_auc\")\n",
    "train_scores, test_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Данные для Validation Curve\n",
    "rf = RandomForestClassifier(n_estimators=100, criterion='gini', max_depth=None, random_state=42)\n",
    "\n",
    "param_range = np.arange(10, 101, 10)\n",
    "\n",
    "train_scores, test_scores = validation_curve(rf, X_train, y_train, param_name=\"n_estimators\", param_range=param_range, cv=5, scoring=\"f1\")\n",
    "train_scores, test_scores"
   ]
  }
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