Python sklearn 交叉驗證

from sklearn.datasets import load_boston

from sklearn.model_selection import cross_val_score
from sklearn.tree import decisiontreeregressor
boston = load_boston()
regressor = decisiontreeregressor(random_state=0) # 例項化
# 交叉驗證有5個引數
# 第乙個引數：可以是任何例項化後的演算法模型；
# 第二個引數：不需要劃分測試集和驗證集的資料；第三個引數：完整的不需要劃分的標籤
# 第四個引數：把資料分為10份，預設是5，通常也選擇5
# 第五個引數：scoring 返回的結果的型別，對於回歸預設為r的平方，可選：neg_mean_squared_error
result = cross_val_score(regressor, boston.data, boston.target, cv=10)
print(result)

分析：對於decisiontreeregressor，回歸樹介面 score 預設返回的是

from sklearn.datasets import load_boston

from sklearn.model_selection import cross_val_score
from sklearn.tree import decisiontreeregressor
boston = load_boston()
regressor = decisiontreeregressor(random_state=0) # 例項化
result = cross_val_score(regressor, boston.data, boston.target, cv=10, scoring='neg_mean_squared_error')
print(result)

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