What are some advanced data splitting strategies for machine learning?

What are some advanced data splitting strategies for machine learning?

Advanced data splitting strategies include techniques like stratified sampling, time-based splitting, and k-fold cross-validation.

Can you explain stratified sampling and when to use it?

Stratified sampling ensures that each class is proportionally represented in the training and test sets, ideal for imbalanced datasets.

What is time-based splitting and how does it differ from random splitting?

Time-based splitting involves dividing data based on time sequences, which is crucial for time series data, unlike random splitting.

How does k-fold cross-validation improve model evaluation?

K-fold cross-validation improves model evaluation by reducing variance and providing a more reliable estimate of model performance.