0300-classification-basic-output.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "🌱 인프런 📚 모두의 한국어 텍스트 분석과 자연어처리 with 파이썬 🐍 https://inf.run/FX4TP\n",
    "\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/corazzon/python-text-analysis/blob/main/0300-classification-basic-output.ipynb)\n",
    "\n",
    "<img src=\"https://i.imgur.com/b6wcQ7f.png\" width=\"500\">\n",
    "\n",
    "\n",
    "* scikit-learn 1.0 이상의 버전을 사용해 주세요!\n",
    "* 1.0 이하의 버전이라면 conda prompt 혹은 터미널을 열어 아래 명령어로 업그레이드 해주세요!\n",
    "* conda : conda update scikit-learn\n",
    "* pip install scikit-learn --upgrade\n",
    "\n",
    "\n",
    "* https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.3.2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 사이킷런이 1.0으로 넘어오면서 많은 기능이 업데이트 되었습니다.\n",
    "# sklearn 1.0 이상의 버전을 사용해 주세요. \n",
    "import sklearn\n",
    "\n",
    "sklearn.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://i.imgur.com/rKOYbeX.png\">\n",
    "\n",
    "전체 과정\n",
    "* feature_names : 학습(훈련), 예측에 사용할 단어 사전입니다.\n",
    "* label_name : 정답값\n",
    "\n",
    "* X_train : 학습(훈련)에 사용할 데이터셋 예) 시험의 기출문제\n",
    "\n",
    "* X_test :  예측에 사용할 데이터셋 예) 실전 시험문제\n",
    "\n",
    "* y_train : 학습(훈련)에 사용할 정답 값 예) 기출문제의 정답\n",
    "\n",
    "* model : 학습, 예측에 사용할 머신러닝 알고리즘\n",
    "\n",
    "* model.fit(X_train, y_train) : 학습(훈련), 기출문제와 정답을 가지고 학습(훈련)하는 과정과 유사합니다.\n",
    "\n",
    "* model.predict(X_test) :  예측, 실제 시험을 보는 과정과 유사합니다. => 문제를 풀어서 정답을 구합니다.\n",
    "\n",
    "* score\n",
    "    * 시험을 봤다면 몇 문제를 맞고 틀렸는지 채점해 봅니다.\n",
    "* metric\n",
    "    * 점수를 채점하는 공식입니다. (예를 들어 학교에서 중간고사를 봤다면 전체 평균을 계산해 줍니다.)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 한글 폰트 설정을 위해 설치\n",
    "# !pip install koreanize-matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 분석을 위한 pandas, 수치계산을 위한 numpy, 시각화를 위한 seaborn, matplotlib, koreanize_matplotlib 을 로드합니다.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import koreanize_matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 그래프 품질 설정\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 예제 문서 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus = [\"코로나 거리두기와 코로나 상생지원금 문의입니다.\",\n",
    "          \"지하철 운행시간과 지하철 요금 문의입니다.\",\n",
    "          \"지하철 승강장 문의입니다.\",\n",
    "          \"코로나 선별진료소 문의입니다.\",\n",
    "          \"버스 운행시간 문의입니다.\", \n",
    "          \"버스 터미널 위치 안내입니다.\",\n",
    "          \"코로나 거리두기 안내입니다.\",\n",
    "          \"택시 승강장 문의입니다.\"\n",
    "         ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>문서</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>코로나 거리두기와 코로나 상생지원금 문의입니다.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>지하철 운행시간과 지하철 요금 문의입니다.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>지하철 승강장 문의입니다.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>코로나 선별진료소 문의입니다.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>버스 운행시간 문의입니다.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>버스 터미널 위치 안내입니다.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>코로나 거리두기 안내입니다.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>택시 승강장 문의입니다.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           문서\n",
       "0  코로나 거리두기와 코로나 상생지원금 문의입니다.\n",
       "1     지하철 운행시간과 지하철 요금 문의입니다.\n",
       "2              지하철 승강장 문의입니다.\n",
       "3            코로나 선별진료소 문의입니다.\n",
       "4              버스 운행시간 문의입니다.\n",
       "5            버스 터미널 위치 안내입니다.\n",
       "6             코로나 거리두기 안내입니다.\n",
       "7               택시 승강장 문의입니다."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pandas 의 DataFrame 으로 만듭니다.\n",
    "df = pd.DataFrame(corpus, columns=[\"문서\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 정답 데이터 만들기\n",
    "* 코로나가 들어가면 \"보건\"으로 그 외는 \"교통\"으로 레이블링"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2     True\n",
       "3    False\n",
       "4     True\n",
       "5     True\n",
       "6    False\n",
       "7     True\n",
       "Name: 문서, dtype: bool"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# str.contains 를 통해 특정 텍스트가 들어가는 여부를 알 수 있음\n",
    "# \"코로나\" 가 들어가는 텍스트 찾기\n",
    "df[\"문서\"].str.contains(\"코로나\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loc[행, 열]\n",
    "# loc[조건, 열]\n",
    "# loc[조건, 열] = 대체값 으로 특정 열에 해당하는 값을 변경\n",
    "# \"코로나\"가 포함이면 \"보건\"으로 나머지는 \"교통\" 으로 정답을 레이블링\n",
    "df[\"정답\"] = \"\"\n",
    "\n",
    "df.loc[df[\"문서\"].str.contains(\"코로나\"), \"정답\"] = \"보건\"\n",
    "df.loc[~df[\"문서\"].str.contains(\"코로나\"), \"정답\"] = \"교통\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>문서</th>\n",
       "      <th>정답</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>코로나 거리두기와 코로나 상생지원금 문의입니다.</td>\n",
       "      <td>보건</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>지하철 운행시간과 지하철 요금 문의입니다.</td>\n",
       "      <td>교통</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>지하철 승강장 문의입니다.</td>\n",
       "      <td>교통</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>코로나 선별진료소 문의입니다.</td>\n",
       "      <td>보건</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>버스 운행시간 문의입니다.</td>\n",
       "      <td>교통</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>버스 터미널 위치 안내입니다.</td>\n",
       "      <td>교통</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>코로나 거리두기 안내입니다.</td>\n",
       "      <td>보건</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>택시 승강장 문의입니다.</td>\n",
       "      <td>교통</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           문서  정답\n",
       "0  코로나 거리두기와 코로나 상생지원금 문의입니다.  보건\n",
       "1     지하철 운행시간과 지하철 요금 문의입니다.  교통\n",
       "2              지하철 승강장 문의입니다.  교통\n",
       "3            코로나 선별진료소 문의입니다.  보건\n",
       "4              버스 운행시간 문의입니다.  교통\n",
       "5            버스 터미널 위치 안내입니다.  교통\n",
       "6             코로나 거리두기 안내입니다.  보건\n",
       "7               택시 승강장 문의입니다.  교통"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 잘 만들어졌는지 확인하기\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "정답\n",
       "교통    5\n",
       "보건    3\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 정답 값 빈도수 확인하기\n",
    "df[\"정답\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='정답', ylabel='count'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 430,
       "width": 552
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x=\"정답\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 텍스트 데이터 수치 형태로 변경하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<8x16 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 27 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sklearn.feature_extraction.text의 CountVectorizer 로 BOW 벡터화\n",
    "# fit_transform 으로 변환하기\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "cvect = CountVectorizer()\n",
    "X = cvect.fit_transform(df[\"문서\"])\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DTM(document-term matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 피처 가져오기\n",
    "# get_feature_names is deprecated in 1.0 and will be removed in 1.2\n",
    "# feature_names\n",
    "# cvect.get_feature_names()\n",
    "df_dtm = pd.DataFrame(X.toarray(), columns=cvect.get_feature_names_out())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>거리두기</th>\n",
       "      <th>거리두기와</th>\n",
       "      <th>문의입니다</th>\n",
       "      <th>버스</th>\n",
       "      <th>상생지원금</th>\n",
       "      <th>선별진료소</th>\n",
       "      <th>승강장</th>\n",
       "      <th>안내입니다</th>\n",
       "      <th>요금</th>\n",
       "      <th>운행시간</th>\n",
       "      <th>운행시간과</th>\n",
       "      <th>위치</th>\n",
       "      <th>지하철</th>\n",
       "      <th>코로나</th>\n",
       "      <th>택시</th>\n",
       "      <th>터미널</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   거리두기  거리두기와  문의입니다  버스  상생지원금  선별진료소  승강장  안내입니다  요금  운행시간  운행시간과  위치  지하철  \\\n",
       "0     0      1      1   0      1      0    0      0   0     0      0   0    0   \n",
       "1     0      0      1   0      0      0    0      0   1     0      1   0    2   \n",
       "2     0      0      1   0      0      0    1      0   0     0      0   0    1   \n",
       "3     0      0      1   0      0      1    0      0   0     0      0   0    0   \n",
       "4     0      0      1   1      0      0    0      0   0     1      0   0    0   \n",
       "5     0      0      0   1      0      0    0      1   0     0      0   1    0   \n",
       "6     1      0      0   0      0      0    0      1   0     0      0   0    0   \n",
       "7     0      0      1   0      0      0    1      0   0     0      0   0    0   \n",
       "\n",
       "   코로나  택시  터미널  \n",
       "0    2   0    0  \n",
       "1    0   0    0  \n",
       "2    0   0    0  \n",
       "3    1   0    0  \n",
       "4    0   0    0  \n",
       "5    0   0    1  \n",
       "6    1   0    0  \n",
       "7    0   1    0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dtm(document-term matrix)\n",
    "df_dtm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 문제와 정답 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>거리두기</th>\n",
       "      <th>거리두기와</th>\n",
       "      <th>문의입니다</th>\n",
       "      <th>버스</th>\n",
       "      <th>상생지원금</th>\n",
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       "      <th>승강장</th>\n",
       "      <th>안내입니다</th>\n",
       "      <th>요금</th>\n",
       "      <th>운행시간</th>\n",
       "      <th>운행시간과</th>\n",
       "      <th>위치</th>\n",
       "      <th>지하철</th>\n",
       "      <th>코로나</th>\n",
       "      <th>택시</th>\n",
       "      <th>터미널</th>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   거리두기  거리두기와  문의입니다  버스  상생지원금  선별진료소  승강장  안내입니다  요금  운행시간  운행시간과  위치  지하철  \\\n",
       "0     0      1      1   0      1      0    0      0   0     0      0   0    0   \n",
       "1     0      0      1   0      0      0    0      0   1     0      1   0    2   \n",
       "2     0      0      1   0      0      0    1      0   0     0      0   0    1   \n",
       "3     0      0      1   0      0      1    0      0   0     0      0   0    0   \n",
       "4     0      0      1   1      0      0    0      0   0     1      0   0    0   \n",
       "5     0      0      0   1      0      0    0      1   0     0      0   1    0   \n",
       "6     1      0      0   0      0      0    0      1   0     0      0   0    0   \n",
       "7     0      0      1   0      0      0    1      0   0     0      0   0    0   \n",
       "\n",
       "   코로나  택시  터미널  \n",
       "0    2   0    0  \n",
       "1    0   0    0  \n",
       "2    0   0    0  \n",
       "3    1   0    0  \n",
       "4    0   0    0  \n",
       "5    0   0    1  \n",
       "6    1   0    0  \n",
       "7    0   1    0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 독립변수로 사용할 X 변수에 dtm array 를 할당\n",
    "# 예) 시험문제\n",
    "X = df_dtm\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    보건\n",
       "1    교통\n",
       "2    교통\n",
       "3    보건\n",
       "4    교통\n",
       "5    교통\n",
       "6    보건\n",
       "7    교통\n",
       "Name: 정답, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 종속변수로 사용할 y 변수에 정답값인 \"category\" 값을 할당\n",
    "# 예) 시험의 정답 값\n",
    "y = df[\"정답\"]\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터셋 나누기\n",
    "* 데이터셋을 나눌 때 가장 흔히 하는 실수는 무엇일까요?\n",
    "* train, test 를 잘못 나누는 일이에요! 문제와 정답의 수가 맞는지 확인해 보세요!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 8:2 의 비율로 구하기 위해 전체 데이터의 행에서 80% 위치에 해당되는 값을 구해서 split_count 라는 변수에 담습니다.\n",
    "split_count = int(df.shape[0] * 0.8)\n",
    "split_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((6, 16), (6,))"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 학습 세트 만들기 예) 시험의 기출문제\n",
    "# 학습 세트의 정답 만들기 예) 기출문제의 정답\n",
    "# X_train, y_train\n",
    "X_train = X[:split_count]\n",
    "y_train = y[:split_count]\n",
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2, 16), (2,))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 예측 세트 만들기 예) 실전 시험문제\n",
    "# 예측 세트의 정답 만들기 예) 실전 문제의 정답\n",
    "# X_test, y_test\n",
    "X_test = X[split_count:]\n",
    "y_test = y[split_count:]\n",
    "X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 머신러닝 모델 로드하기\n",
    "\n",
    "* [1.10. Decision Trees — scikit-learn documentation](https://scikit-learn.org/stable/modules/tree.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DecisionTreeClassifier 를 사용합니다.\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "model = DecisionTreeClassifier()\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 학습(훈련)\n",
    "* 시험을 볼 때 기출문제(X_train)와 정답(y_train)을 보고 공부하는 과정과 유사합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit(기출문제, 정답)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 예측\n",
    "* 실전 시험문제(X_test)라고 보면 됩니다. 우리가 정답을 직접 예측합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['보건', '교통'], dtype=object)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict(실전문제)\n",
    "y_predict = model.predict(X_test)\n",
    "y_predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 평가\n",
    "### 정확도(Accuracy) 측정하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 예측의 정확도를 구합니다. 100점 만점 중에 몇 점을 맞았는지 구한다고 보면 됩니다.\n",
    "(y_test == y_predict).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 위에서 처럼 직접 구할 수도 있지만 미리 구현된 알고리즘을 가져와 사용합니다.\n",
    "# sklearn.metrics 의 accuracy_score 를 가져옵니다.\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "accuracy_score(y_test,  y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>교통</th>\n",
       "      <th>보건</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>정답</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>교통</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>보건</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0  교통  보건\n",
       "정답           \n",
       "교통      1   0\n",
       "보건      0   1"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# crosstab 으로 confusion matrix 를 구합니다.\n",
    "pd.crosstab(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0],\n",
       "       [0, 1]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confusion_matrix 를 통해 구할 수도 있습니다.\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 트리 알고리즘 분석하기\n",
    "* 의사결정나무를 시각화 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- 코로나 <= 0.50\n",
      "|   |--- class: 교통\n",
      "|--- 코로나 >  0.50\n",
      "|   |--- class: 보건\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# export_text 로 트리 분석하기\n",
    "from sklearn.tree import export_text\n",
    "\n",
    "# help(export_text)\n",
    "et = export_text(model, feature_names=cvect.get_feature_names_out())\n",
    "print(et)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 389,
       "width": 515
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot_tree 로 시각화 하기\n",
    "from sklearn.tree import plot_tree\n",
    "\n",
    "# help(plot_tree)\n",
    "plot_tree(model, feature_names=cvect.get_feature_names_out(), rounded=True, filled=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "거리두기     0.0\n",
       "거리두기와    0.0\n",
       "문의입니다    0.0\n",
       "버스       0.0\n",
       "상생지원금    0.0\n",
       "선별진료소    0.0\n",
       "승강장      0.0\n",
       "안내입니다    0.0\n",
       "요금       0.0\n",
       "운행시간     0.0\n",
       "운행시간과    0.0\n",
       "위치       0.0\n",
       "지하철      0.0\n",
       "코로나      1.0\n",
       "택시       0.0\n",
       "터미널      0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# feature_importances_로 피처 중요도 확인하기\n",
    "fi = pd.Series(model.feature_importances_)\n",
    "fi.index = cvect.get_feature_names_out()\n",
    "fi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  },
  "toc": {
   "base_numbering": 1,
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   "number_sections": false,
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