AttributeError 'GridSearchCV‘对象没有属性'cv_results_’
AttributeError 'GridSearchCV‘对象没有属性'cv_results_’
提问于 2022-06-25 17:33:38
我正在为一个科学学习教程研究load_boston()数据。我遇到了这个属性错误：
AttributeError 'GridSearchCV' object has no attribute 'cv_results_'有没有人知道是否有窃听器？我正在使用1.1.1版本的科学知识-学习。
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()回答 1
Stack Overflow用户
发布于 2022-06-25 20:58:57
要点是，cv_results_是已安装的GridSearchCV实例的属性，而您只安装了管道(其基本估计器)。因此，您应该安装mod1以使其工作。
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，.fit() of GridSearchCV方法并不返回拟合的基本估计量(当然，尽管进行了拟合)。因此，如果您只是通过pipe.predict(X)替代pipe.fit(X, y)，您将无法调用mod1.fit(X, y)。
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/72755994
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