Commit v0.0.3
Training with two ML models to find the optimal framework for accuracy and training speed.
XGBoost, an ensemble learning algorithm, stands out for its efficiency and accuracy. It builds upon decision trees, iteratively refining predictions and reducing errors. LightGBM, another ensemble technique, utilizes a leaf-wise growth strategy, resulting in faster training and lighter models compared to XGBoost.
However, the performance of these models can be further enhanced through feature engineering and hyperparameter tuning. Feature engineering involves transforming and creating new features from existing data to capture relevant patterns and improve model interpretability. Hyperparameter tuning, on the other hand, involves selecting the optimal values for the model's parameters to optimize its performance.
Hyperparameter tuning involves optimizing parameters like learning rate, regularization, and tree depth. Optuna an open source hyperparameter optimization framework to automate hyperparameter search
