Hyperparameter Tuning and Grid Search Explained

Every machine learning model has settings that it doesn't learn directly from data, called hyperparameters, and choosing the right values can significantly affect performance.

Examples include the number of neighbors in KNN, the depth of a decision tree, or the learning rate in gradient boosting.

Grid Search is one of the most common tuning methods. It tests every possible combination of specified hyperparameter values, then picks the best-performing combination.

For instance, If testing 3 values for tree depth and 3 values for learning rate, grid search would train and evaluate 9 separate models to find the strongest combination.

While thorough, grid search can become slow on large parameter sets, which is why it is often paired with cross validation to ensure the chosen values genuinely generalize well.