Articles on: Algorithms


CatBoost is a gradient boosting algorithm developed by Yandex. Although it is specifically designed to handle categorical data, which is data that consists of categorical variables, such as gender or product categories, it can also work with numerical, and text features.

CatBoost works by combining multiple decision trees to make a prediction. Each decision tree is trained on a different subset of the data, and the final prediction is made by combining the predictions of each individual tree. The algorithm uses gradient boosting to optimize the parameters of each tree and to weight their contributions to the final prediction.

Unlike traditional gradient boosting algorithms, CatBoost can handle categorical variables natively without the need for one-hot encoding. This allows the algorithm to learn the relationships between the categorical variables and the target variable directly, which can improve the accuracy of the prediction.

Another feature of CatBoost is its built-in regularization, which helps to prevent overfitting and to improve the generalization of the model. This regularization is implemented through a combination of techniques such as feature selection, random sampling, and penalization of large weights.

CatBoost has been shown to perform well on a variety of datasets and has been used in a number of industries, including finance, healthcare, and e-commerce. It has several advantages over traditional gradient boosting algorithms, including faster training times, improved handling of categorical data, and better performance on noisy and imbalanced datasets.

Updated on: 30/01/2023

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