k_fold_cross_validation.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split, cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
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       "          X    Y\n",
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   "source": [
    "X=np.random.normal(0,1,100)\n",
    "Y=np.array([0]*90+[1]*10)\n",
    "\n",
    "np.random.shuffle(Y)\n",
    "\n",
    "df=pd.DataFrame(np.vstack((X,Y)).transpose(),columns=['X','Y'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "df.Y.value_counts()"
   ]
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       "{1}"
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   "source": [
    "{1,2,3} - {2,3}"
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   "execution_count": 5,
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     "execution_count": 5,
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   "source": [
    "k=5\n",
    "\n",
    "np.random.seed(0)\n",
    "idx=np.arange(len(df))\n",
    "np.random.shuffle(idx)\n",
    "\n",
    "TRAIN_PCT=1-1/k\n",
    "VAL_PCT=1-TRAIN_PCT\n",
    "\n",
    "TRAIN_SIZE=int(len(df)*TRAIN_PCT)\n",
    "VAL_SIZE=len(df)-TRAIN_SIZE\n",
    "\n",
    "SCORE=[]\n",
    "\n",
    "clf=LogisticRegression(random_state=0)\n",
    "for i in range(k):\n",
    "    val_idx=idx[VAL_SIZE*i:VAL_SIZE*(i+1)]\n",
    "    train_idx=list(set(idx)-set(val_idx))\n",
    "    \n",
    "    train_df=df.loc[train_idx]\n",
    "    val_df=df.loc[val_idx]\n",
    "    \n",
    "    X_train=train_df[['X']]\n",
    "    y_train=train_df['Y']\n",
    "    \n",
    "    X_val=val_df[['X']]\n",
    "    y_val=val_df['Y']\n",
    "    \n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred=clf.predict(X_val)\n",
    "    SCORE.append(accuracy_score(y_val, y_pred))\n",
    "    \n",
    "np.mean(np.array(SCORE))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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   "source": [
    "SCORE"
   ]
  },
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   "execution_count": 7,
   "metadata": {},
   "outputs": [
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     "execution_count": 7,
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   "source": [
    "X=df[['X']]\n",
    "y=df['Y']\n",
    "\n",
    "X_train, X_test, y_train, y_test=train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred=clf.predict(X_test)\n",
    "\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=df[['X']]\n",
    "y=df['Y']\n",
    "\n",
    "cross_val_score(clf, X, y, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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