Day_042_HW.ipynb

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    "## 作業\n",
    "\n",
    "1. 試著調整 DecisionTreeClassifier(...) 中的參數，並觀察是否會改變結果？\n",
    "2. 改用其他資料集 (boston, wine)，並與回歸模型的結果進行比較"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets, metrics\n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "wine = datasets.load_wine()\n",
    "x_train, x_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.2, random_state=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acuuracy:  1.0\n"
     ]
    }
   ],
   "source": [
    "reg = DecisionTreeRegressor()\n",
    "reg.fit(x_train, y_train)\n",
    "pred = reg.predict(x_test)\n",
    "acc = metrics.accuracy_score(y_test, pred)\n",
    "print(\"Acuuracy: \", acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alcohol',\n",
       " 'malic_acid',\n",
       " 'ash',\n",
       " 'alcalinity_of_ash',\n",
       " 'magnesium',\n",
       " 'total_phenols',\n",
       " 'flavanoids',\n",
       " 'nonflavanoid_phenols',\n",
       " 'proanthocyanins',\n",
       " 'color_intensity',\n",
       " 'hue',\n",
       " 'od280/od315_of_diluted_wines',\n",
       " 'proline']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sklearn dataset內建\n",
    "wine.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importance:  [0.         0.         0.02084517 0.00276839 0.         0.\n",
      " 0.6168095  0.         0.         0.10905869 0.         0.\n",
      " 0.25051825]\n"
     ]
    }
   ],
   "source": [
    "print(\"Feature importance: \", reg.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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