Python 中的多项式交叉验证

问题描述

我正在尝试交叉验证 python 中的一些东西，但使用这个：

from sklearn.model_selection import cross_val_score

在上下文中，我正在创建以下模型：

coef = poly.polyfit(train["X_training"],train["Y_training"],4)
model = poly.polynomial(coef)

现在我正在尝试以这种方式交叉验证

from sklearn.model_selection import cross_val_score
cross_val_score(model,test["X_test"],test["Y_test"],cv=5)

但我的错误是这样的：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-186-5bcb7ab926a4> in <module>
      1 from sklearn.model_selection import cross_val_score
----> 2 cross_val_score(model,cv=0)

~\anaconda3\envs\TensonFlow\lib\site-packages\sklearn\utils\validation.py in inner_f(*args,**kwargs)
     70                           FutureWarning)
     71         kwargs.update({k: arg for k,arg in zip(sig.parameters,args)})
---> 72         return f(**kwargs)
     73     return inner_f
     74 

~\anaconda3\envs\TensonFlow\lib\site-packages\sklearn\model_selection\_validation.py in cross_val_score(estimator,X,y,groups,scoring,cv,n_jobs,verbose,fit_params,pre_dispatch,error_score)
    397     """
    398     # To ensure multimetric format is not supported
--> 399     scorer = check_scoring(estimator,scoring=scoring)
    400 
    401     cv_results = cross_validate(estimator=estimator,X=X,y=y,groups=groups,~\anaconda3\envs\TensonFlow\lib\site-packages\sklearn\utils\validation.py in inner_f(*args,args)})
---> 72         return f(**kwargs)
     73     return inner_f
     74 

~\anaconda3\envs\TensonFlow\lib\site-packages\sklearn\metrics\_scorer.py in check_scoring(estimator,allow_none)
    423             return None
    424         else:
--> 425             raise TypeError(
    426                 "If no scoring is specified,the estimator passed should "
    427                 "have a 'score' method. The estimator %r does not."

TypeError: If no scoring is specified,the estimator passed should have a 'score' method. The estimator polynomial([ 6.0000592,8.02956741,-5.99141415,-3.00869471,1.99588109],domain=[-1,1],window=[-1,1]) does not.

我做错了什么吗？

解决方法

看起来正确交叉验证的方法是使用

from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
test = test.dropna()
poly_features = PolynomialFeatures(degree=grade)
X_poly = poly_features.fit_transform(test)
poly = LinearRegression()
cross_val_score(poly,X_poly,test["Y_test"],cv=5)

cross-validation polynomials python regression