The following notebook is a work in progress.
This notebook aims to be a template for classification or regression tasks by stacking three machine learning models together. Using the hyper-optimisation library: Optuna, three of the above models are chosen, scaled uniquely and tunned. These three predictions are then tunned inside the above equation.
Models used in this notebook:
- Multi-layer Perceptron
- K-Neighbours
- Support Vector
- Gaussian Process + Radial-Basis Function
- Gaussian Naïve Bayes
- Quadratic Discriminant Analysis
- Linear Regression
- Lasso
- Ridge
- Ada Boost
- Extra Trees
- Random Forest
- XGBoost
- LightGBM
Areas for possible improvement:
- Additional parameters to tune