Cross-validation techniques
Cross-validation techniques Data Science Project
Introduction to Supervised Learning with scikit-learn

Cross-validation techniques

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.
Start this project
Cross-validation techniquesCross-validation techniques
Project Created by

Verónica Barraza

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

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.

multiplechoice

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.

multiplechoice

Compute the average precision score of the cross-validation result

multiplechoice

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.

multiplechoice

True or False: Cross-validation can only be used to estimate the precision score of classification models, but not regression models.

Cross-validation techniquesCross-validation techniques
Project Created by

Verónica Barraza

This project is part of

Introduction to Supervised Learning with scikit-learn

Explore other projects