Methods for Hyperparameter Tuning in Decision Tree

To optimize the model’s performance it is important to tune the hyperparameters. There are three most widely used methods available such as grid search, random search, and Bayesian optimization, these searches explore the different combinations of hyperparameter values that help to find the most effective configuration and fine-tune the decision tree model.

Grid search

Grid search is a fundamental method for hyperparameter tuning that exhaustively searches through the predefined hyperparameters. It evaluates all possible combinations and makes it a reliable approach for finding the optimal hyperparameter. Grid search often leads to highly accurate predictions when adequate computational resources are available.

It can run in parallel since each trail runs independently without influencing the time sequence. However, the drawback of grid search is its computational cost, especially when dealing with high-dimensional parameter spaces.

Randomized Search

It performs the randomized search over hyperparameters from certain distributions over possible parameter values. The random search’s searching process continues until the predetermined limit or the desired accuracy is achieved. This search yields finer results than the grid search because of the two benefits.

• Independent allocation of limit: Unlike grid search where the limit for each hyperparameter set is a fixed value, the random search holds the power of assigning the limit independently according to the distribution in the search space. Therefore, the random search may perform better in the case where the hyperparameters are not uniformly distributed.
• Easy parallelization: When there is a longer search time in the grid search it is not guaranteed that it will lead to better results. But in the random search flexible resource allocation and easy parallelization accredit the effectiveness of the model.

Bayesian Optimization

Bayesian optimization is a sequential model-based method where the goal is to find the global optimum with the minimum number of trails. It is a kind of search where the probabilistic model helps to identify the best set of hyperparameters. Compared to grid and random search, it requires fewer attempts to find the best hyperparameter set which makes it a computationally efficient optimization technique. Even though it is computationally efficient, it is more complex to use compared to the grid and random search.

For most cases, Grid Search or Randomized Search are good starting points due to their ease of implementation. If computational cost is a major concern and you have a large search space, consider exploring Bayesian Optimization.

Decision trees are powerful models extensively used in machine learning for classification and regression tasks. The structure of decision trees resembles the flowchart of decisions helps us to interpret and explain easily. However, the performance of decision trees highly relies on the hyperparameters, selecting the optimal hyperparameter can significantly impact the model’s accuracy, generalization ability, and robustness.

In this article, we will explore the different ways to tune the hyperparameters and their optimization techniques with the help of decision trees.