LightGBM content

Machine Learning

Go to portfolio

LightGBM

Chapter-1: Gradient boosting & LightGBM basics

Gradient boosting is a machine learning technique based on boosting in a functional space, where the target is pseudo-residuals instead of residuals as in traditional boosting.
Chapter-2: Decision Tree

A decision tree partitions the feature space into rectangular regions through a series of binary splits, and assigns a constant prediction to each region (leaf).
Chapter-3: LightGBM: XGBoost — Second-Order Gradient Boosting

A decision tree partitions the feature space into rectangular regions through a series of binary splits, and assigns a constant prediction to each region (leaf).
Chapter-4: LightGBM core concept

A decision tree partitions the feature space into rectangular regions through a series of binary splits, and assigns a constant prediction to each region (leaf).
Chapter-5: Parameters, Training & Tuning

A decision tree partitions the feature space into rectangular regions through a series of binary splits, and assigns a constant prediction to each region (leaf).
Chapter-6: LightGBM: Mathematical Appendix & The Full Picture

A decision tree partitions the feature space into rectangular regions through a series of binary splits, and assigns a constant prediction to each region (leaf).

References

Repositories

Books

Machine Learning Using Python — Manarajan Pradhan, U. Dinesh Kumar
Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow — Aurélien Géron
Statistics — Murray R. Spiegel, Larry J. Stephens

Tutorials & Learning Paths