scikit-learn实现简单的决策树

#encoding=utf-8
import numpy as np
import pandas as  pd

def main():
    #Pre-processing
    from sklearn.datasets import load_iris
    iris = load_iris()
    print(iris)
    print(len(iris["data"]))
    # from sklearn.cross_validation import train_test_split
    from sklearn.model_selection import train_test_split
    train_data,test_data,train_traget,test_target=train_test_split(iris.data,iris.target,test_size=0.2,random_state=1)

    #Model
    from sklearn import tree
    clf = tree.DecisionTreeClassifier(criterion="entropy")
    clf.fit(train_data,train_traget)
    y_pred = clf.predict(test_data)

    #Verify
    from sklearn import metrics
    print(metrics.accuracy_score(y_true=test_target,y_pred=y_pred))#分类准确率分数是指所有分类正确的百分比
    print(metrics.confusion_matrix(y_true=test_target,y_pred=y_pred))#混淆矩阵

    #文件目录写自己的
    with open("./python_source/tree.doc","w") as fw:
        tree.export_graphviz(clf,out_file=fw)

if __name__ == '__main__':
    main()