原數據

根據 ‘buy’, ‘maintain’, ‘doors’, ‘persons’, ‘boot’, ‘safety’, ‘accept’

購買”、“維護”、“門”、“人”、“系統”、“安全” 來判斷顧客是否接受買車

# -*- coding:utf-8 -*-import pandas as pd import numpy as np from sklearn.linear_model import LogisticRegressionCV from sklearn import metrics from sklearn.model_selection import train_test_split from sklearn.preprocessing import label_binarize import matplotlib as mpl import matplotlib.pyplot as plt from sklearn.ensemble import RandomForestClassifierif __name__ == '__main__':pd.set_option('display.width', 300)pd.set_option('display.max_columns', 300)data = pd.read_csv('car.data', header=None)n_columns = len(data.columns)columns = ['buy', 'maintain', 'doors', 'persons', 'boot', 'safety', 'accept']new_columns = dict(list(zip(np.arange(n_columns), columns)))data.rename(columns=new_columns, inplace=True)print(data.head(10))# one-hot編碼x = pd.DataFrame()for col in columns[:-1]:t = pd.get_dummies(data[col])t = t.rename(columns=lambda x: col+'_'+str(x))x = pd.concat((x, t), axis=1)print(x.head(10))print(x.shape)y = np.array(pd.Categorical(data['accept']).codes)x, x_test, y, y_test = train_test_split(x, y, test_size=0.3)clf = LogisticRegressionCV(Cs=np.logspace(-3, 4, 8), cv=5)# clf = RandomForestClassifier(n_estimators=50, max_depth=7)clf.fit(x, y)y_hat = clf.predict(x)print('訓練集精確度：', metrics.accuracy_score(y, y_hat))y_test_hat = clf.predict(x_test)print('測試集精確度：', metrics.accuracy_score(y_test, y_test_hat))n_class = len(np.unique(y))if n_class > 2:y_test_one_hot = label_binarize(y_test, classes=np.arange(n_class))y_test_one_hot_hat = clf.predict_proba(x_test)fpr, tpr, _ = metrics.roc_curve(y_test_one_hot.ravel(), y_test_one_hot_hat.ravel())print('Micro AUC:\t', metrics.auc(fpr, tpr))auc = metrics.roc_auc_score(y_test_one_hot, y_test_one_hot_hat, average='micro')print('Micro AUC(System):\t', auc)auc = metrics.roc_auc_score(y_test_one_hot, y_test_one_hot_hat, average='macro')print('Macro AUC:\t', auc)else:fpr, tpr, _ = metrics.roc_curve(y_test.ravel(), y_test_hat.ravel())print('AUC:\t', metrics.auc(fpr, tpr))auc = metrics.roc_auc_score(y_test, y_test_hat)print('AUC(System):\t', auc)mpl.rcParams['font.sans-serif'] = 'SimHei'mpl.rcParams['axes.unicode_minus'] = Falseplt.figure(figsize=(8, 7), facecolor='w')plt.plot(fpr, tpr, 'r-', lw=2, label='AUC=%.4f' % auc)plt.legend(loc='lower right', fontsize=14)plt.xlim((-0.01, 1.02))plt.ylim((-0.01, 1.02))plt.xticks(np.arange(0, 1.1, 0.1))plt.yticks(np.arange(0, 1.1, 0.1))plt.xlabel('False Positive Rate', fontsize=14)plt.ylabel('True Positive Rate', fontsize=14)plt.grid(b=True, ls=':')plt.title('ROC曲線和AUC', fontsize=16)plt.show()

y

獨熱編碼：x的數據

buy_high buy_low buy_med buy_vhigh maintain_high maintain_low maintain_med maintain_vhigh doors_2 doors_3 doors_4 doors_5more persons_2 persons_4 persons_more boot_big boot_med boot_small safety_high safety_low safety_med 0 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 1 2 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 1 1 0 0 3 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 1 0 0 1 0 4 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 1 0 0 0 1 5 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 1 0 1 0 0 6 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 1 0 7 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0 1 8 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 1 0 0 1 0 0 9 0 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0 0 1 0 1 0

LogisticRegressionCV

訓練集精確度： 0.9081885856079405
測試集精確度： 0.8921001926782274
Micro AUC: 0.9834757073221438
Micro AUC(System): 0.9834757073221438
Macro AUC: 0.9668852150116471

RandomForestClassifier

訓練集精確度： 0.9545078577336642
測試集精確度： 0.9075144508670521
Micro AUC: 0.9924203330598466
Micro AUC(System): 0.9924203330598466
Macro AUC: 0.988179483976249

總結

以上是生活随笔為你收集整理的汽车购买分类的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。