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Is there a way we can grid-search multiple estimators at a time in Sklearn or any other library. For example can we pass SVM and Random Forest in one grid search ?.
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Pipeline can be used to chain multiple estimators into one. This is useful as there is often a fixed sequence of steps in processing the data, for example feature selection, normalization and classification.
The only difference between both the approaches is in grid search we define the combinations and do training of the model whereas in RandomizedSearchCV the model selects the combinations randomly. Both are very effective ways of tuning the parameters that increase the model generalizability.
One of the most important and generally-used methods for performing hyperparameter tuning is called the exhaustive grid search. This is a brute-force approach because it tries all of the combinations of hyperparameters from a grid of parameter values.
Yes. Example:
pipeline = Pipeline([ ('vect', CountVectorizer()), ('clf', SGDClassifier()), ]) parameters = [ { 'vect__max_df': (0.5, 0.75, 1.0), 'clf': (SGDClassifier(),), 'clf__alpha': (0.00001, 0.000001), 'clf__penalty': ('l2', 'elasticnet'), 'clf__n_iter': (10, 50, 80), }, { 'vect__max_df': (0.5, 0.75, 1.0), 'clf': (LinearSVC(),), 'clf__C': (0.01, 0.5, 1.0) } ] grid_search = GridSearchCV(pipeline, parameters) 
from sklearn.base import BaseEstimator from sklearn.model_selection import GridSearchCV class DummyEstimator(BaseEstimator): def fit(self): pass def score(self): pass # Create a pipeline pipe = Pipeline([('clf', DummyEstimator())]) # Placeholder Estimator # Candidate learning algorithms and their hyperparameters search_space = [{'clf': [LogisticRegression()], # Actual Estimator 'clf__penalty': ['l1', 'l2'], 'clf__C': np.logspace(0, 4, 10)}, {'clf': [DecisionTreeClassifier()], # Actual Estimator 'clf__criterion': ['gini', 'entropy']}] # Create grid search gs = GridSearchCV(pipe, search_space) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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