分类模型——决策树

2019年8月15日 212次阅读 来源: 叶青婧

Split train and test

from sklearn.cross_validation import train_test_split
x_train, x_test, y_train, y_test = train_test_split(customer.ix[:,0:customer.columns.size-1], customer.ix[:,customer.columns.size-1], test_size = 0.2)
x_train, x_test, y_train, y_test = train_test_split(order.ix[:,0:order.columns.size-1], order.ix[:,order.columns.size-1], test_size = 0.2)

Decision tree

使用信息熵作为划分标准，对决策树进行训练

from sklearn import tree  
clf = tree.DecisionTreeClassifier(criterion='entropy')
print(clf)  
clf.fit(x_train, y_train)

Predict

answer = pd.Series(clf.predict(x_test))
result = pd.DataFrame(np.c_[y_test, answer])
result['correct'] = np.where(result[0] == result[1], 1, 0) 
sum(result['correct'])/pd.count(result['correct'])
result.sum()/result.count()

preds = clf.predict(x_test)
result = pd.crosstab(y_test, preds, rownames=['actual'], colnames=['preds'])
res = result.div(result.sum(1).astype(float), axis=0)
res
res2 = res.drop(max(res) < 0.5, axis=1)
res.to_csv('result.csv', index = True, header = True)
    原文作者：叶青婧
    原文地址: https://segmentfault.com/a/1190000013579461
    本文转自网络文章，转载此文章仅为分享知识，如有侵权，请联系博主进行删除。
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