Optuna is an open-source hyperparameter optimization framework designed to automate the hyperparameter search process for machine learning models. It supports various frameworks like TensorFlow, Keras, Scikit-Learn, XGBoost, and LightGBM, offering features like eager search spaces, state-of-the-art algorithms, and easy parallelization.
This article explains permutation feature importance (PFI), a popular method for understanding feature importance in explainable AI. The author walks through calculating PFI from scratch using Python and XGBoost, discussing the rationale behind the method and its limitations.
- Extreme Gradient Boosting: A quick and reliable regressor and classifier
- Summary: LightGBM is faster and better though XGBoost is close
- Summary: LightGBM is faster and better though XGBoost is close
A Comprehensive Guide to Understand and Implement Text Classification in Python
