AttributeError：'GridSearchCV'对象没有属性'cv

AttributeError：'GridSearchCV'对象没有属性'cv_results_'(AttributeError: 'GridSearchCV' object has no attribute 'cv_results_')

我尝试应用此代码：

pipe = make_pipeline(TfidfVectorizer(min_df=5), LogisticRegression()) param_grid = {'logisticregression__C': [ 0.001, 0.01, 0.1, 1, 10, 100], "tfidfvectorizer__ngram_range": [(1, 1),(1, 2),(1, )]} grid = GridSearchCV(pipe, param_grid, cv=5) grid.fit(text_train, Y_train) scores = _results_['mean_test_score'].reshape(-1, ).T # visualize heat map heatmap = heatmap( scores, xlabel="C", ylabel="ngram_range", cmap="viridis", fmt="%.f", xticklabels=param_grid['logisticregression__C'], yticklabels=param_grid['tfidfvectorizer__ngram_range']) (heatmap)

但我有这个错误：

AttributeError: 'GridSearchCV' object has no attribute 'cv_results_'

提前致谢

I try to apply this code :

pipe = make_pipeline(TfidfVectorizer(min_df=5), LogisticRegression()) param_grid = {'logisticregression__C': [ 0.001, 0.01, 0.1, 1, 10, 100], "tfidfvectorizer__ngram_range": [(1, 1),(1, 2),(1, )]} grid = GridSearchCV(pipe, param_grid, cv=5) grid.fit(text_train, Y_train) scores = _results_['mean_test_score'].reshape(-1, ).T # visualize heat map heatmap = heatmap( scores, xlabel="C", ylabel="ngram_range", cmap="viridis", fmt="%.f", xticklabels=param_grid['logisticregression__C'], yticklabels=param_grid['tfidfvectorizer__ngram_range']) (heatmap)

But I have this error :

AttributeError: 'GridSearchCV' object has no attribute 'cv_results_'

最满意答案

更新你的scikit-learn， cv_results_已在0.18.1中引入，之前它被称为grid_scores_并且结构略有不同http:///0.17/modules/generated/sklearn.grid_search.GridSearchCV.html#sklearn .grid_search.GridSearchCV

Solved ! Uninstall and install conda scikit learn in 0.18.1 How to upgrade scikit-learn package in anaconda.

When I import GridSearch :

from _selection import GridSearchCV