IBM Data Science Test 2025 – 400 Free Practice Questions to Pass the Exam

The purpose of `train_test_split` in Scikit-learn is to divide a dataset into two distinct sets: one for training the model and the other for testing its performance. This is a crucial step in the machine learning workflow, as it allows for an unbiased evaluation of a model's success.

By splitting the dataset, typically into a training set and a testing set, you can train your algorithm on one portion of the data while reserving another portion to assess how well the model generalizes to unseen data. This helps ensure that the model does not simply memorize the training data, which could result in poor performance when making predictions on new data. The function allows users to specify the proportion of the dataset to include in the training or testing sets, making it a flexible tool for preparing data for modeling tasks.

Using `train_test_split` is essential for validating the performance of a machine learning model, ensuring that the results are meaningful and robust.