Selecting the Right Cross-Validation Method in Machine Learning

Learn how to select the appropriate cross-validation method for your machine learning model in this 11-minute tutorial. Discover a foolproof approach to choosing the right cross-validation methodology every time. Explore various scenarios including handling large datasets with hold-out validation, using k-fold cross-validation for independent data points, applying time-split for time-dependent data, and utilizing group-fold for group-dependent information. Gain insights into best practices and access additional resources on cross-validation techniques to enhance your machine learning projects.

- Introduction:
- Trick to select the right cross-validation method:
- Lots of data use hold-out:
- Independent data point use kfold:
- Time dependent use timesplit :
- Group dependent use groupfold:
- Summary: