Check the list of available parameters with `estimator.get_params().keys()`

Question

When I try to run a RandomForestClassifier with Pipeline and param_grid:

pipeline = Pipeline([("scaler" , StandardScaler()),
                     ("rf",RandomForestClassifier())])
from sklearn.model_selection import GridSearchCV
param_grid = {
    'max_depth': [4, 5, 10],
    'max_features': [2, 3],
    'min_samples_leaf': [3, 4, 5],
    'n_estimators': [100, 200, 300]
}
# initialize
grid_pipeline = GridSearchCV(pipeline,param_grid,n_jobs=-1, verbose=1, cv=3, scoring='f1')
# fit
grid_pipeline.fit(X_train,y_train)
grid_pipeline.best_params_

I get the following error:

ValueError: Invalid parameter max_depth for estimator Pipeline(memory=None,
         steps=[('scaler',
                 StandardScaler(copy=True, with_mean=True, with_std=True)),
                ('rf',
                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,
                                        class_weight=None, criterion='gini',
                                        max_depth=None, max_features='auto',
                                        max_leaf_nodes=None, max_samples=None,
                                        min_impurity_decrease=0.0,
                                        min_impurity_split=None,
                                        min_samples_leaf=1, min_samples_split=2,
                                        min_weight_fraction_leaf=0.0,
                                        n_estimators=100, n_jobs=None,
                                        oob_score=False, random_state=None,
                                        verbose=0, warm_start=False))],
         verbose=False). Check the list of available parameters with `estimator.get_params().keys()`.

Although I have reviewed the scikit learn documentation and several posts, I can't find the error in my code.

Answer 1

When you use a pipeline with GridSearchCV() you must include names in parameter keys. Just separate names from parameter names with a double underscore. In your case:

param_grid = {
    'rf__max_depth': [4, 5, 10],
    'rf__max_features': [2, 3],
    'rf__min_samples_leaf': [3, 4, 5],
    'rf__n_estimators': [100, 200, 300]
}

Example from sklearn documentation: https://scikit-learn.org/stable/tutorial/statistical_inference/putting_together.html