sklearn import tree
# visualize code
from sklearn.externals.six import StringIO
import pydotplus
# 决策树算法...# 初步的两个特性的判断,[重量,表皮光滑度](对于水果,可以是:1=光滑,0=粗糙)
# 结论标签,1=苹果,0=橘子
features = [[140,1],[130,1],[150,0],[170,0...[130,0]]
features_names = ['重量','表皮光滑度']
labels = [0, 0, 1, 1, 0, 1]
label_name = ['橘子','苹果']
#调用决策树算法的核心语句...tree.DecisionTreeClassifier()
dt= dt.fit(features, labels)
#测试数据,预测[200,1]
print(dt.predict([[200,1]]))
# 可以根据测试数据,得到预测的结果...150,0],[170,0],[150,1],[130,0]]
features_names = ['重量','表皮光滑度']
labels = [0, 0, 1, 0, 0, 1]
则[200,0]的预测结果是