Issue
After running a GridSearchCV
, I would like to see the score for each parameter combination.
How can I access the scores for each parameter combinations after running GridSearchCV
?
Here is an example code that I used in another post.
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.grid_search import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.naive_bayes import MultinomialNB
X_train = ['qwe rtyuiop', 'asd fghj kl', 'zx cv bnm', 'qw erty ui op', 'as df ghj kl', 'zxc vb nm', 'qwe rt yu iop', 'asdfg hj kl', 'zx cvb nm',
'qwe rt yui op', 'asd fghj kl', 'zx cvb nm', 'qwer tyui op', 'asd fg hjk l', 'zx cv b nm', 'qw ert yu iop', 'as df gh jkl', 'zx cvb nm',
'qwe rty uiop', 'asd fghj kl', 'zx cvbnm', 'qw erty ui op', 'as df ghj kl', 'zxc vb nm', 'qwe rtyu iop', 'as dfg hj kl', 'zx cvb nm',
'qwe rt yui op', 'asd fg hj kl', 'zx cvb nm', 'qwer tyuiop', 'asd fghjk l', 'zx cv b nm', 'qw ert yu iop', 'as df gh jkl', 'zx cvb nm']
y_train = ['1', '2', '3', '1', '1', '3', '1', '2', '3',
'1', '2', '3', '1', '4', '1', '2', '2', '4',
'1', '2', '3', '1', '1', '3', '1', '2', '3',
'1', '2', '3', '1', '4', '1', '2', '2', '4']
parameters = {
'clf__alpha': (1e-1, 1e-2),
'vect__ngram_range': [(1,2),(1,3)],
'vect__max_df': (0.9, 0.98)
}
text_clf_Pipline_MultinomialNB = Pipeline([('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', MultinomialNB()),
])
gs_clf = GridSearchCV(text_clf_Pipline_MultinomialNB, parameters, n_jobs=-1)
gs_classifier = gs_clf.fit(X_train, y_train)
Solution
Yes it does, exactly as it is stated in the docs:
grid_scores_
: list of named tuplesContains scores for all parameter combinations in param_grid. Each entry corresponds to one parameter setting. Each named tuple has the attributes:
parameters
, a dict of parameter settingsmean_validation_score
, the mean score over the cross-validation foldscv_validation_scores
, the list of scores for each fold
Answered By - lejlot
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