More than one estimator in GridSearchCV(sklearn)

GridSearchCV works on parameters. It will train multiple estimators (but same
class (one of SVC, or DecisionTreeClassifier, or other classifiers) with
different parameter combinations from specified in

param_grid

best_estimator_

So essentially

best_estimator_

So in the basic setup you cannot use multiple estimators in the grid-search.

But as a workaround, you can have multiple estimators when using a pipeline in
which the estimator is a

"parameter"

Something like this:

from sklearn.pipeline import Pipelinefrom sklearn.svm import SVCfrom sklearn.tree import DecisionTreeClassifierfrom sklearn.model_selection import GridSearchCVfrom sklearn.datasets import load_irisiris_data = load_iris()X, y = iris_data.data, iris_data.target# Just initialize the pipeline with any estimator you like    pipe = Pipeline(steps=[('estimator', SVC())])# Add a dict of estimator and estimator related parameters in this listparams_grid = [{     'estimator':[SVC()],     'estimator__C': [1, 10, 100, 1000],     'estimator__gamma': [0.001, 0.0001],     },     {     'estimator': [DecisionTreeClassifier()],     'estimator__max_depth': [1,2,3,4,5],     'estimator__max_features': [None, "auto", "sqrt", "log2"],     },    # {'estimator':[Any_other_estimator_you_want],    #  'estimator__valid_param_of_your_estimator':[valid_values]   ]grid = GridSearchCV(pipe, params_grid)

You can add as many dicts inside the list of

params_grid

'estimator'