homework/Day_038_HW.ipynb

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    "## [作業重點]\n",
    "使用 Sklearn 中的線性迴歸模型，來訓練各種資料集，務必了解送進去模型訓練的**資料型態**為何，也請了解模型中各項參數的意義"
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   "source": [
    "## 作業\n",
    "試著使用 sklearn datasets 的其他資料集 (wine, boston, ...)，來訓練自己的線性迴歸模型。"
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   "source": [
    "### HINT: 注意 label 的型態，確定資料集的目標是分類還是回歸，在使用正確的模型訓練！"
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets, linear_model\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score, accuracy_score"
   ]
  },
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   "execution_count": 2,
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   "source": [
    "wine = datasets.load_wine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "X = wine.data\n",
    "y = wine.target"
   ]
  },
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   "source": [
    "# 切分訓練集/測試集\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 4)"
   ]
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   "source": [
    "logreg = linear_model.LogisticRegression()"
   ]
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     "text": [
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
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     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
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     "execution_count": 7,
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   "source": [
    "logreg.fit(x_train, y_train)"
   ]
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   "execution_count": 8,
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   "source": [
    "y_pred = logreg.predict(x_test)"
   ]
  },
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   "execution_count": 9,
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     "text": [
      "Accuracy:  0.9444444444444444\n"
     ]
    }
   ],
   "source": [
    "acc = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy: \", acc)"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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