During this lab, we will learn about the K-Fold Cross-Validation technique, which plays a crucial role in assessing the performance of machine learning models and ensuring their generalization to unseen data.
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.
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True or False: In cross-validation, the data is split into training and testing sets, and the precision score is computed on the testing set for each fold.
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True or False: Increasing the number of folds in cross-validation can improve the accuracy of the estimated precision score, but at the cost of increased computational time.
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Compute the average precision score of the cross-validation result
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True or False: The results show that the Random Forest model trained with n_estimators=100 and random_state=42 did not generalize well, as it showed poor performance on the test dataset.
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True or False: Cross-validation can only be used to estimate the precision score of classification models, but not regression models.
Author
Verónica Barraza
This project is part of
Introduction to Supervised Learning with scikit-learn
