What is data splitting and why is it important in machine learning?

What is data splitting and why is it important in machine learning?

Data splitting is the process of dividing your dataset into subsets, typically training, validation, and test sets. It's crucial because it helps to evaluate the model's performance and avoid overfitting.

How do I decide the size of each split?

The size of each split can depend on the total dataset size and the specific problem. A common practice is to use 70% for training, 15% for validation, and 15% for testing.

Can you explain overfitting and how data splitting helps with it?

Overfitting occurs when a model learns the training data too well, including noise. Data splitting helps by ensuring the model is tested on unseen data, which provides a better estimate of its performance.

What techniques can I use to ensure my splits are representative?

You can use stratified sampling to ensure that each split maintains the same distribution of classes as the original dataset. This is especially important for imbalanced datasets.