Machine Learning Project

In this project, the aim is to develop using only Numpy tree models such as decision trees, bagging, random forests, gradient boosting and others

Modules

• base:

  • Contains BinaryTreeNode which creates a one shot node (parent node) and two connected children nodes (left node and right node)
  • Contains the DecisionTreeClassifier model which splits the dataset iteratively based on given criterion. It includes model fittig, prediction and visualisation of tree

• metrics: Stores various metrics used for:

  • Regression:
    • Mean Squared Error (MSE)
    • Mean Absolute Error (MAE)
  • Classification:
    • Accuracy
    • Recall
    • Precision
    • F1 Score
  • Others:
    • Confusion Matrix
    • Cross Validation

• err_handl: Manages errors within the modules.

• dgp: Generates data with different characteristics used for linear regression and logistic regression

Testing

All the work is thoroughly tested and summarized in the notebook folder. These notebooks execute, test, and evaluate tree models.

Versions

• Python Version: 3.11.4
• Numpy Version : 1.26.4
• Pandas Version : 2.2.1