Train-Test Split Explained

A train-test split divides your dataset into two parts: one for training the model and one for testing it, so you can check how well the model performs on data it has never seen before.

Why train-test split matters

• It prevents overfitting by testing on unseen data.
• It gives an honest measure of how the model will perform in the real world.
• It is a required step before evaluating any machine learning model.

Splitting the dataset

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

test_size=0.2 means 20% of the data is used for testing, while the remaining 80% is used for training. random_state=42 ensures the split is the same every time you run the code, making results reproducible.

A proper train-test split ensures your model is evaluated fairly and honestly, rather than judged on data it already memorised.