機器學習4-評分

直接貼代碼

# coding=utf-8

from sklearn.datasets import load_iris

# 獲取鳶尾數據
iris = load_iris()

X = iris.data
y = iris.target

# 評分公式
from sklearn.metrics import accuracy_score

# cross_validation 改成 model_selection
# 前者好像是版本問題，過期了
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=.3)


# 決策樹測試
# 成功率在90%+
from sklearn import tree
clf = tree.DecisionTreeClassifier()
clf.fit(X_train,y_train)

predictions = clf.predict(X_test)
print 'decision tree score:',accuracy_score(y_test,predictions)


# k-neighbors
from sklearn import neighbors
clf = neighbors. KNeighborsClassifier()
clf.fit(X_train,y_train)

predictions = clf.predict(X_test)
print 'k-neighbors score:',accuracy_score(y_test,predictions)

附圖