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Grid search CV for model complexity

In the last slide, you saw how most classifiers have one or more hyperparameters that control its complexity. You also learned to tune them using GridSearchCV(). In this exercise, you will perfect this skill. You will experiment with:

  • The number of trees, n_estimators, in a RandomForestClassifier.
  • The maximum depth, max_depth, of the decision trees used in an AdaBoostClassifier.
  • The number of nearest neighbors, n_neighbors, in KNeighborsClassifier.

Diese Übung ist Teil des Kurses

Designing Machine Learning Workflows in Python

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Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Set a range for n_estimators from 10 to 40 in steps of 10
param_grid = {'____': range(10, ____, ____)}

# Optimize for a RandomForestClassifier() using GridSearchCV
grid = GridSearchCV(____, param_grid, cv=3)
grid.fit(X, y)
grid.best_params_
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