AttributeError＆＃x27; GridSearchCV＆＃x27;对象没有属性＆＃x27; cv_results _＆＃x27;

Question

我正在通过Scikit-Learn教程的load_boston（）数据进行工作。我正在遇到此属性错误：

AttributeError 'GridSearchCV' object has no attribute 'cv_results_'

有人知道是否有错误？我正在使用1.1.1版本的Scikit-Learn。

import sklearn
from sklearn.datasets import load_boston
from sklearn.neighbors import KNeighborsRegressor
from sklearn.preprocessing import StandardScaler   
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV    
from sklearn.linear_model import LinearRegression
import matplotlib.pylab as plt 
import pandas as pd 
from sklearn.model_selection import cross_val_score

print(sklearn.__version__)

X, y = load_boston(return_X_y=True)      

mod = KNeighborsRegressor().fit(X, y)     

pipe = Pipeline([
    ("scale", StandardScaler()),
    ("model", KNeighborsRegressor(n_neighbors=3))  
    ])
print(pipe.get_params())  

mod1 = GridSearchCV(estimator=pipe, param_grid={'model__n_neighbors': [1,2,3,4,5,6,7,8,9,10]},cv = 3)

pipe.fit(X, y)    
pred = pipe.predict(X)  
df = pd.DataFrame(mod1.cv_results_)   

plt.scatter(pred, y)  #pred instead of X
plt.title("Boston Housing Market")
plt.show()

Answer 1

要点是cv_results _是 fitting gridSearchcv实例的属性，而您仅安装了管道（其基本估算器）。因此，您应该适合mod1使其正常工作。

import sklearn
from sklearn.datasets import load_boston
from sklearn.neighbors import KNeighborsRegressor
from sklearn.preprocessing import StandardScaler   
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV    
from sklearn.linear_model import LinearRegression
import matplotlib.pylab as plt 
import pandas as pd 
from sklearn.model_selection import cross_val_score

X, y = load_boston(return_X_y=True)      

mod = KNeighborsRegressor().fit(X,y)     

pipe = Pipeline([
    ("scale", StandardScaler()),
    ("model", KNeighborsRegressor(n_neighbors=3))  
])
print(pipe.get_params())   
mod1 = GridSearchCV(estimator=pipe,param_grid={'model__n_neighbors': 
    [1,2,3,4,5,6,7,8,9,10]},cv = 3)
mod1.fit(X, y)

df = pd.DataFrame(mod1.cv_results_)

但是请注意，该方法.fit（） gridSearchCV不会返回拟合的基本估计器（当然，尽管拟合了）。因此，您将无法调用pipe.predict（x），如果您只替换pipe> pipe.fit（x，y） via mod1.fit（ x，y）。