The Earnix Blog

Amid rising costs and fierce competition, insurers must harness advanced analytics and dynamic pricing to achieve sustainable profitability in 2025 and beyond.

Discover how Earnix’s Hierarchical Level Selector intelligently merges categorical hierarchies to create cleaner, faster, and more accurate predictive models. This innovative feature helps insurers and banks simplify complex data structures, improving both model performance and explainability.

Discover how Smart Grouping revolutionizes the way insurers and banks tackle categorical features, improving model clarity and efficiency through the innovative Smart Grouping technique in Auto-GLM, leading to more interpretable and credible analytical outcomes.

XGBoost (Extreme Gradient Boosting) builds on the idea of boosting with several optimizations, such as regularization techniques to control overfitting, parallelization to improve computation speed, and handling sparse data more efficiently. These improvements allow XGBoost to achieve high performance, making it one of the most popular machine learning algorithms in practice today.