GridSearchCV and RandomizedSearchCV
GridSearchCV and RandomizedSearchCV Data Science Project
Introduction to Supervised Learning with scikit-learn

GridSearchCV and RandomizedSearchCV

The project will cover topics such as understanding hyperparameters, the impact they have on model performance, and how to tune them to achieve the best results.
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Project Activities

All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.

All our activities include solutions with explanations on how they work and why we chose them.

multiplechoice

Which value has the Best hyperparameters of max_depth?

multiplechoice

Use GridSearchCV to search over a range of values for max_depth (from 1 to 20) and n_estimators (from 1 to 10) hyperparameters to find the combination that yields the best performance.

For this task use cv=5, and random_state=42 and compute the Best mean score

multiplechoice

True or False: For this example, the best hyperparameter obtained is max_depth = 19

GridSearchCV and RandomizedSearchCVGridSearchCV and RandomizedSearchCV
Author

Verónica Barraza

This project is part of

Introduction to Supervised Learning with scikit-learn

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