What is train-test split in machine learning?

What is train-test split in machine learning?

Train-test split is a technique used to evaluate the performance of a machine learning model by dividing the dataset into two parts: one for training the model and the other for testing its performance.

Why is it important to split the data?

It's important to split the data to ensure that the model can generalize well to unseen data. Training on the entire dataset can lead to overfitting, where the model performs well on training data but poorly on new data.

How do you typically split the data?

Typically, the data is split into a training set and a test set, often in a ratio like 80/20 or 70/30, depending on the size of the dataset.

Can I use cross-validation instead?

Yes, cross-validation is another technique that can be used to assess model performance. It involves splitting the data into multiple subsets and training/testing the model multiple times to get a more reliable estimate.