In this project, I will tune the hyperparameters of two of the following model types:

• Regression Tree
• Random Forest
• Gradient Boosted Machine (GBM)

Hyperparameters are parameters that are set before training the model and affect the behavior of the model during training. Tuning hyperparameters involves selecting the best combination of hyperparameters that lead to the highest performance of the model on the given dataset.

To tune the hyperparameters, I’ll need to experiment with different values for each hyperparameter and evaluate the performance of the model for each combination of hyperparameters. I’ll use techniques such as cross-validation to evaluate the performance of the model on different subsets of the data.

Once I have selected the best hyperparameters for each model, I’ll need to evaluate the performance of the models on a holdout dataset. This dataset is separate from the dataset used for tuning the hyperparameters and is used to estimate the generalization performance of the models.

Overall, the goal of this project is to demonstrate my ability to apply machine learning techniques to real-world problems by tuning the hyperparameters of these models to achieve the best possible performance.