Catboost Model

CatBoost is a gradient boosting algorithm particularly effective for handling categorical data and achieving high predictive accuracy across diverse applications. Current research focuses on integrating CatBoost into hybrid models, such as combining it with wavelet transforms for time series forecasting or incorporating it into zero-inflated models for improved insurance claim prediction. This versatile algorithm finds use in various fields, including financial fraud detection, air quality prediction, and water quality monitoring, demonstrating its significant impact on both scientific modeling and practical problem-solving.