SoniSiddharth/ML-Decision-Trees-from-scratch

Machine Learning - Decision tree model implementation from scratch

Decision Trees and Random Forests ⭐🌳⭐

Directory Structure: 📁

│   ADABoost_test.py
│   assignment_q4_subjective_answers.md
│   Bagging_test.py
│   estate-experiments.py
│   experiments.py
│   iris-experiments.py
│   Makefile
│   metrics.py
│   Random_forest.py
│   random_forest_iris.py
│   README.md
│   realestate.csv
│   usage.py
│
├───ensemble
│       ADABoost.py
│       bagging.py
│       __init__.py
│
├───images
│
└───tree
        base.py
        randomForest.py
        utils.py
        __init__.py

Instructions to run 🏃

make help make decision_tree make iris make real_estate make experiments make adaboost make bagging make random_forest make random_forest_iris

Time and Sampling plots: ⏰

DIDO Training

DIDO Prediction

DIRO Training

DIRO Prediction

RIDO Training

RIDO Prediction

RIRO Training

RIRO Prediction

Adaboost: 💥

Decision Tree Estimator Plot

All Estimators Individual Decision Surface

Combined Decision Surfaces

• IRIS Dataset

Decision Tree Estimator PLot

All Estimators Individual Decision Surface

Combined Decision Surfaces

Bagging: 👊

Decision Tree Estimator Plot

All Estimators Individual Decision Surface

Random Forest: 🌳🌳

Decision Tree Estimator Structure

All Estimators Individual Decision Surface

Combined Decision Surfaces