Different types of Cross-validation in Machine Learning

Classification in Depth with Scikit-Learn

Different types of Cross-validation in Machine Learning

The project will cover topics such as the benefits of cross-validation, its applications in ensemble models, and how it can be used to achieve the best performance of a model on new data. By the end of the project, the you will have gained practical experience in using cross-validation techniques to improve the performance of ensemble models (or any machine learning model) and learned how to apply these techniques to real-world problems.

Start this project

Project Created by

Verónica Barraza

View Profile

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.

Project Created by

Verónica Barraza

View Profile

This project is part of

Classification in Depth with Scikit-Learn

Explore Skill Track

Explore other projects

Different types of Cross-validation in Machine Learning

Verónica Barraza

Project Activities

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.

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.

Compute the average precision score of the cross-validation result

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.

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

Verónica Barraza

Classification in Depth with Scikit-Learn

Set Operations using Sakila

LIKE Operator using World

Membership and Range Operators with World Database