What is data partitioning in machine learning?

What is data partitioning in machine learning?

Data partitioning in machine learning refers to the process of dividing a dataset into subsets for training, validation, and testing.

Why is it important to partition data?

Partitioning data helps to evaluate the model's performance accurately and prevents overfitting by ensuring the model generalizes well to unseen data.

What are common methods for data partitioning?

Common methods include random sampling, stratified sampling, and k-fold cross-validation.

How do I choose the right partitioning method?

The choice depends on the dataset size, the problem type, and whether you need to maintain the distribution of classes in your partitions.