07장_모형평가_6절_분류_회귀_군집.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 분류"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 정확도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "#import numpy as np\n",
    "from sklearn.metrics import accuracy_score\n",
    "y_pred = [0, 2, 1, 3]\n",
    "y_true = [0, 1, 2, 3]\n",
    "print(accuracy_score(y_true, y_pred))\n",
    "print(accuracy_score(y_true, y_pred, normalize=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## confusionm matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 0, 0],\n",
       "       [0, 0, 1],\n",
       "       [1, 0, 2]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "y_true = [2, 0, 2, 2, 0, 1]\n",
    "y_pred = [0, 0, 2, 2, 0, 2]\n",
    "confusion_matrix(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## classification report "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "     class 0       0.67      1.00      0.80         2\n",
      "     class 1       0.00      0.00      0.00         1\n",
      "     class 2       1.00      0.50      0.67         2\n",
      "\n",
      "    accuracy                           0.60         5\n",
      "   macro avg       0.56      0.50      0.49         5\n",
      "weighted avg       0.67      0.60      0.59         5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "y_true = [0, 1, 2, 2, 0]\n",
    "y_pred = [0, 0, 2, 1, 0]\n",
    "target_names = ['class 0', 'class 1', 'class 2']\n",
    "print(classification_report(y_true, y_pred, target_names=target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 회귀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mean absolute error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "y_true = [3, -0.5, 2, 7]\n",
    "y_pred = [2.5, 0.0, 2, 8]\n",
    "\n",
    "print(mean_absolute_error(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mean squared error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.375"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "y_true = [3, -0.5, 2, 7]\n",
    "y_pred = [2.5, 0.0, 2, 8]\n",
    "print(mean_squared_error(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9486081370449679\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "y_true = [3, -0.5, 2, 7]\n",
    "y_pred = [2.5, 0.0, 2, 8]\n",
    "print(r2_score(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 군집"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adjusted rand index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.24242424242424246\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import adjusted_rand_score\n",
    "labels_true = [0, 0, 0, 1, 1, 1]\n",
    "labels_pred = [0, 0, 1, 1, 2, 2]\n",
    "\n",
    "print(adjusted_rand_score(labels_true, labels_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# silloutte score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5789497702625118\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "X = [[1, 2], [4, 5], [2, 1], [6, 7], [2, 3]]\n",
    "labels = [0, 1, 0, 1, 0] \n",
    "sil_score = silhouette_score(X, labels)\n",
    "print(sil_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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