数据集[1] 提取码：j50c
数据长这样：
完整代码：
代码语言：javascript复制from sklearn.tree import DecisionTreeClassifier,export_graphviz
from sklearn.preprocessing import LabelBinarizer
from sklearn.feature_extraction import DictVectorizer
import pydotplus
labelBinarizer=LabelBinarizer() #方便后面对标签二值化后的标签进行复原
def load_file():
#读取数据集
data = open('ensemble/lenses.txt')
lenses = [];label = ['age','prescript','astigmatic','tearRate']
feature= [];labels = []
for line in data.readlines():
lenses.append(line.strip().split('t'))
for i in range(len(lenses)):
row = {}
for j in range(0,len(lenses[i])-1):
row[label[j]] = lenses[i][j]
feature.append(row)
labels.append(lenses[i][len(lenses[i]) - 1])
train_x = DictVectorizer().fit_transform(feature).toarray() #特征提取
train_y = labelBinarizer.fit_transform(labels) #标签二值化
re_label=train_y #同样方便后面输出预测结果
#前2/3(16个)做训练集，后1/3(8个)做测试集
test_x = train_x[int(len(train_x)*2/3):len(train_x)]
test_y = train_y[int(len(train_y)*2/3):len(train_y)]
train_x = train_x[0:int(len(train_x)*2/3)]
train_y = train_y[0:int(len(train_y)*2/3)]
return train_x,train_y,test_x,test_y,re_label
def decision_Tree():
train_x, train_y, test_x, test_y, relabel = load_file()
clf = DecisionTreeClassifier()
clf = clf.fit(train_x,train_y) #训练模型
#可视化
dot_data = export_graphviz(clf, out_file=None,
feature_names = clf.feature_importances_,
filled = True, rounded = True, special_characters = True)
graph = pydotplus.graph_from_dot_data(dot_data)
graph.write_pdf('tree.pdf')
#预测
pred=clf.predict(test_x)
original_data=labelBinarizer.inverse_transform(relabel) #标签二值化后复原
original_data=original_data[int(len(original_data)*2/3):len(original_data)] #只截取测试集部分
#输出
for i in range(len(test_x)):
print('正确类别:',original_data[i],'预测类别:',original_data[test_y.tolist().index(pred.tolist()[i])])
print('分类正确率为:',clf.score(test_x,test_y))
if __name__ == '__main__':
decision_Tree()
决策树：
输出结果：
References
[1] 数据集: https://pan.baidu.com/s/1DOqNzeeEAEG84OlhBTZfZg
