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.
