使用pipelineand确定最佳参数后GridSearchCV，我pickle/joblib这个过程如何在以后重用？当它是一个单一的分类器时，我看到了如何做到这一点......
from sklearn.externals import joblib
joblib.dump(clf, 'filename.pkl')
pipeline但是，在执行和完成 a 之后，如何以最佳参数整体保存它gridsearch？
我试过了：
joblib.dump(grid, 'output.pkl')- 但这抛弃了每次网格搜索尝试（许多文件）joblib.dump(pipeline, 'output.pkl')- 但我不认为它包含最好的参数
X_train = df['Keyword']
y_train = df['Ad Group']
pipeline = Pipeline([
('tfidf', TfidfVectorizer()),
('sgd', SGDClassifier())
])
parameters = {'tfidf__ngram_range': [(1, 1), (1, 2)],
'tfidf__use_idf': (True, False),
'tfidf__max_df': [0.25, 0.5, 0.75, 1.0],
'tfidf__max_features': [10, 50, 100, 250, 500, 1000, None],
'tfidf__stop_words': ('english', None),
'tfidf__smooth_idf': (True, False),
'tfidf__norm': ('l1', 'l2', None),
}
grid = GridSearchCV(pipeline, parameters, cv=2, verbose=1)
grid.fit(X_train, y_train)
#These were the best combination of tuning parameters discovered
##best_params = {'tfidf__max_features': None, 'tfidf__use_idf': False,
## 'tfidf__smooth_idf': False, 'tfidf__ngram_range': (1, 2),
## 'tfidf__max_df': 1.0, 'tfidf__stop_words': 'english',
## 'tfidf__norm': 'l2'}