Random Forest with GridSearchCV - Error on param_grid

Question

Im trying to create a Random Forest model with GridSearchCV but am getting an error pertaining to param_grid: "ValueError: Invalid parameter max_features for estimator Pipeline. Check the list of available parameters with `estimator.get_params().keys()". I'm classifying documents so I am also pushing tf-idf vectorizer to the pipeline. Here is the code:

from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, f1_score, accuracy_score, precision_score, confusion_matrix
from sklearn.pipeline import Pipeline

 #Classifier Pipeline
pipeline = Pipeline([
    ('tfidf', TfidfVectorizer()),
    ('classifier', RandomForestClassifier())
])
# Params for classifier
params = {"max_depth": [3, None],
              "max_features": [1, 3, 10],
              "min_samples_split": [1, 3, 10],
              "min_samples_leaf": [1, 3, 10],
              # "bootstrap": [True, False],
              "criterion": ["gini", "entropy"]}

# Grid Search Execute
rf_grid = GridSearchCV(estimator=pipeline , param_grid=params) #cv=10
rf_detector = rf_grid.fit(X_train, Y_train)
print(rf_grid.grid_scores_)

I can't figure out why the error is showing. The same btw is occurring when I run a decision tree with GridSearchCV. (Scikit-learn 0.17)

Answer 1

You have to assign the parameters to the named step in the pipeline. In your case classifier. Try prepending classifier__ to the parameter name. Sample pipeline

params = {"classifier__max_depth": [3, None],
              "classifier__max_features": [1, 3, 10],
              "classifier__min_samples_split": [1, 3, 10],
              "classifier__min_samples_leaf": [1, 3, 10],
              # "bootstrap": [True, False],
              "classifier__criterion": ["gini", "entropy"]}