BE - Machine Learning

The holdout method is a straightforward way to test how well a machine learning model works. You take your entire dataset and split it into two parts: a training set (usually 70-80% of the data) and a test set (20-30% of the data). The model learns from the training set, then you test its performance on the separate test set that it has never seen before. This helps you understand how accurately the model will perform on new, real-world data. It's like studying with most of your notes and then taking a test on questions you've never practiced before to see if you really learned the material.