When should I use train-test split in machine learning?

When should I use train-test split in machine learning?

You should use train-test split to evaluate your model's performance on unseen data, ensuring it generalizes well.

What is the typical ratio for train-test split?

A common ratio is 80/20 or 70/30, where 80% or 70% of the data is used for training and the rest for testing.

Are there any situations where I shouldn't use train-test split?

Yes, if you have a very small dataset, you might consider using cross-validation instead to make the most of your data.

How does train-test split affect model evaluation?

It helps in assessing how well your model performs on new, unseen data, which is crucial for understanding its effectiveness.

Can I use train-test split for time series data?

For time series data, you should use a time-based split to maintain the order of observations, rather than random splitting.