homework/Day_038_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib 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, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,\n",
       "         1.065e+03],\n",
       "        [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,\n",
       "         1.050e+03],\n",
       "        [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00,\n",
       "         1.185e+03],\n",
       "        ...,\n",
       "        [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,\n",
       "         8.350e+02],\n",
       "        [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,\n",
       "         8.400e+02],\n",
       "        [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,\n",
       "         5.600e+02]]),\n",
       " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2]),\n",
       " 'target_names': array(['class_0', 'class_1', 'class_2'], dtype='<U7'),\n",
       " 'DESCR': 'Wine Data Database\\n====================\\n\\nNotes\\n-----\\nData Set Characteristics:\\n    :Number of Instances: 178 (50 in each of three classes)\\n    :Number of Attributes: 13 numeric, predictive attributes and the class\\n    :Attribute Information:\\n \\t\\t- 1) Alcohol\\n \\t\\t- 2) Malic acid\\n \\t\\t- 3) Ash\\n\\t\\t- 4) Alcalinity of ash  \\n \\t\\t- 5) Magnesium\\n\\t\\t- 6) Total phenols\\n \\t\\t- 7) Flavanoids\\n \\t\\t- 8) Nonflavanoid phenols\\n \\t\\t- 9) Proanthocyanins\\n\\t\\t- 10)Color intensity\\n \\t\\t- 11)Hue\\n \\t\\t- 12)OD280/OD315 of diluted wines\\n \\t\\t- 13)Proline\\n        \\t- class:\\n                - class_0\\n                - class_1\\n                - class_2\\n\\t\\t\\n    :Summary Statistics:\\n    \\n    ============================= ==== ===== ======= =====\\n                                   Min   Max   Mean     SD\\n    ============================= ==== ===== ======= =====\\n    Alcohol:                      11.0  14.8    13.0   0.8\\n    Malic Acid:                   0.74  5.80    2.34  1.12\\n    Ash:                          1.36  3.23    2.36  0.27\\n    Alcalinity of Ash:            10.6  30.0    19.5   3.3\\n    Magnesium:                    70.0 162.0    99.7  14.3\\n    Total Phenols:                0.98  3.88    2.29  0.63\\n    Flavanoids:                   0.34  5.08    2.03  1.00\\n    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\\n    Proanthocyanins:              0.41  3.58    1.59  0.57\\n    Colour Intensity:              1.3  13.0     5.1   2.3\\n    Hue:                          0.48  1.71    0.96  0.23\\n    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\\n    Proline:                       278  1680     746   315\\n    ============================= ==== ===== ======= =====\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThis is a copy of UCI ML Wine recognition datasets.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\\n\\nThe data is the results of a chemical analysis of wines grown in the same\\nregion in Italy by three different cultivators. There are thirteen different\\nmeasurements taken for different constituents found in the three types of\\nwine.\\n\\nOriginal Owners: \\n\\nForina, M. et al, PARVUS - \\nAn Extendible Package for Data Exploration, Classification and Correlation. \\nInstitute of Pharmaceutical and Food Analysis and Technologies,\\nVia Brigata Salerno, 16147 Genoa, Italy.\\n\\nCitation:\\n\\nLichman, M. (2013). UCI Machine Learning Repository\\n[http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\\nSchool of Information and Computer Science. \\n\\nReferences\\n----------\\n(1) \\nS. Aeberhard, D. Coomans and O. de Vel, \\nComparison of Classifiers in High Dimensional Settings, \\nTech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of \\nMathematics and Statistics, James Cook University of North Queensland. \\n(Also submitted to Technometrics). \\n\\nThe data was used with many others for comparing various \\nclassifiers. The classes are separable, though only RDA \\nhas achieved 100% correct classification. \\n(RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \\n(All results using the leave-one-out technique) \\n\\n(2) \\nS. Aeberhard, D. Coomans and O. de Vel, \\n\"THE CLASSIFICATION PERFORMANCE OF RDA\" \\nTech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \\nMathematics and Statistics, James Cook University of North Queensland. \\n(Also submitted to Journal of Chemometrics). \\n',\n",
       " 'feature_names': ['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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用wine資料集\n",
    "wine = datasets.load_wine()\n",
    "wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size = 0.2, random_state = 4)\n",
    "\n",
    "regr = linear_model.LogisticRegression()\n",
    "regr.fit(X_train, y_train)\n",
    "y_pred = logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.9722222222222222\n"
     ]
    }
   ],
   "source": [
    "acc = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy: \", acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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