Machine Learning Library

Models explained from first principles — assumptions, learning, weaknesses, and market relevance.

Machine-learning models explained from first principles. The point of this section is to show that I understand model logic, not just how to call a library function.

Every model page answers the same nine questions:

1. What problem does this model solve?
2. What assumptions does it make?
3. What data does it need?
4. How does it learn?
5. What are its strengths?
6. What are its weaknesses?
7. How could it apply to markets?
8. What does the Python code look like?
9. How would I explain it to a supervisor?

Model pages are added one at a time. The map below is the plan.

Foundation models

Linear regression · logistic regression · decision trees · random forests · XGBoost · LightGBM · support vector machines · K-means clustering · Principal Component Analysis · Gaussian Mixture Models.

Deep learning

Neural networks · activation functions · backpropagation · CNNs · RNNs · LSTMs · transformers · attention · embeddings.

Quant-specific machine learning

Feature engineering for returns · walk-forward validation · time-series cross-validation · avoiding data leakage · overfitting in trading systems · model explainability · SHAP values · regime-aware modelling.