CatBoost vs. LightGBM vs. XGBoost

Which is the best algorithm?

Photo by Tingey Injury Law Firm on Unsplash

CatBoost (Category Boosting), LightGBM (Light Gradient Boosted Machine), and XGBoost (eXtreme Gradient Boosting) are all gradient boosting algorithms. Before diving into their similarity and differences in terms of characteristics and performance, we must understand the term ensemble learning and how it relates to gradient boosting.

Table of Contents

1. Ensemble Learning
2. Catboost vs. LightGBM vs. XGBoost Characteristics
3. Improving Accuracy, Speed, and Controlling Overfitting
4. Performance Comparison

Ensemble Learning

Ensemble Learning is a technique that combines predictions from multiple models to get a prediction that would be more stable and generalize better. The idea is to average out different models’ individual mistakes to reduce the risk of overfitting while maintaining strong prediction performance.

In regression, overall prediction is typically the mean of individual tree predictions, whereas, in classification, overall prediction is based on a weighted vote with probabilities averaged across all trees, and the class with the highest probability is the final predicted class.