Hyperparameter Tuning: A Guide to Improving Model Performance

Hyperparameters are parameters or arguments of a model that can be adjusted by the user to optimize the model's performance. In machine learning, tuning hyperparameters is crucial for achieving better results. In this article, we will explore two popular methods for hyper parameter tuning: Grid Search and Random Search.

Grid Search

Grid Search is a systematic approach to hyperparameter tuning where we define a grid of hyperparameter values and test all possible combinations of these values. Let's consider an example with a Decision Tree model:

• criterion: {"gini", "entropy", "log_loss"}
• splitter: {"best", "random"}
• max_depth: {1, 2, 3, 4, 5, 6}

By default, the Decision Tree uses criterion='gini'. However, we might find that the model performs better with criterion='entropy'. To explore these possibilities, we create a grid with all possible combinations of hyperparameters:

• Total combinations: 3 (criterion) 2 (splitter) 6 (max_depth) = 36

Grid Search will systematically test each of these 36 combinations to find the optimal set of hyper parameters that maximize the model's performance.

Random Search

Random Search, on the other hand, does not exhaustively search through all combinations. Instead, it randomly selects a subset of combinations to evaluate. While this approach may not guarantee finding the best hyperparameters, it is more computationally efficient, especially when the search space is large.

Cross-Validation

When tuning hyperparameters, it's crucial to use cross-validation (CV) to evaluate the model's performance. CV helps prevent overfitting by splitting the data into multiple subsets and using each subset as both training and validation data. A common choice is k-fold cross-validation, where the data is divided into k subsets, and the model is trained and evaluated k times, each time using a different subset as the validation set.

For example, with CV=4, the data is divided into four parts: P1, P2, P3, and P4. Each time, three parts are used for training, and one part is used for validation. This process is repeated for all combinations of hyperparameters, ensuring a thorough evaluation of the model's performance.

Finalizing the Model

After evaluating all combinations of hyperparameters using cross-validation, we can select the best performing set of hyperparameters based on the average performance across all folds. For each combination, we calculate its accuracy, and then we take the average of these accuracies to get the final performance metric for the model.

In conclusion, hyperparameter tuning is a crucial step in the machine learning pipeline to optimize model performance. Both Grid Search and Random Search are effective methods for finding the best hyperparameters, with Grid Search being more exhaustive but computationally expensive, and Random Search offering a more efficient alternative. When combined with cross-validation, these techniques help ensure that the model generalises well to unseen data.