====== Assignment: Ensembles ======

**📅 Deadline:** 31.12.2025 23:59

**🏦 Points:** 3
===== Task Description =====
In this assignment, you are tasked with implementing the basic logic for training and evaluating **random forests** and **gradient boosted machines**. The implementation of the weak learners (random trees) is provided to you.

You are provided with a {{ :courses:be4m33ssu:homeworks:hw_ensembling_template_22_11_2024.zip |template}} containing the following files:
  * **data.py**: Contains functions for loading of datasets and implementation of the Dataset class. You do not need to modify this file.
  * **ensembles.py**: Contains implementation of random forests and gradient boosted machines. It includes the methods **build_gbm**, **evaluate** of the GradientBoostedTrees class, and the methods **build_forest**, **evaluate** of the RandomForest class which you need to implement.
  * **main.py**: Runs experiments and saves data for BRUTE. You do not need to modify this file.
  * **tree.py**: Implements random trees. You do not need to modify this file.
  * **utils.py**: Contains helper functions for loading and saving data, plotting experiments, evaluating models and computing metrics. You do not need to modify this file.
  * **test-cases**: A folder containing public test cases to help you verify your implementation before submitting to [[https://cw.felk.cvut.cz/brute/student/course/BE4M33SSU/ensemble|BRUTE]].

Your objective is to implement the methods **build_gbm**, **evaluate** of the GradientBoostedTrees class, and the methods **build_forest**, **evaluate** of the RandomForest class in **ensembles.py**.

<note important>
All python files must be stored in the root of the .zip sent for submission.
</note>

<note important>
Note that the prediction of the GBM is a //weighted sum// not a //weighted mean//
</note>
===== How to Test =====
After completing your implementation, you can test your solution using the following commands before submitting it to BRUTE:

----


== Test Case 1 ==
  python main.py test-cases/public/instances/instance_1.json
Expected output:
  Gradient Boosted Machine:
        train_rmse: Test OK
        test_rmse: Test OK
  Random Forest:
        train_rmse: Test OK
        test_rmse: Test OK

----

== Test Case 2 ==

  python main.py test-cases/public/instances/instance_2.json
Expected output:
  Gradient Boosted Machine:
        train_rmse: Test OK
        test_rmse: Test OK
  Random Forest:
        train_rmse: Test OK
        test_rmse: Test OK


===== Submission Guidelines =====
  * Submit the completed code as a .zip via BRUTE.
  * All python files must be stored in the root of the .zip sent for submission.
  * Make sure your implementation passes the test cases provided above. Good luck! 😊