What is the impact of data splitting in machine learning?

What is the impact of data splitting in machine learning?

Data splitting is crucial as it helps in evaluating the model's performance on unseen data, preventing overfitting.

How do you typically split the data?

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

What happens if I don't split my data?

Not splitting your data can lead to overfitting, where the model performs well on training data but poorly on new data.

Can you explain overfitting in more detail?

Overfitting occurs when a model learns the noise in the training data instead of the actual patterns, resulting in poor generalization.