CatBoost Comparison results with other Boosting Algorithm

What is CatBoost?

CatBoost or Categorical Boosting is an open-source boosting library developed by Yandex. It is designed for use on problems like regression and classification having a very large number of independent features....

Features of CatBoost

Built-in Method for handling categorical features: CatBoost efficiently handles categorical features without requiring preprocessing. This capability eliminates the need to convert non-numeric factors into numerical values, simplifying the data preparation process. Excellent result without parameter tuning: CatBoost aims to provide excellent results without the need for extensive parameter tuning. This feature saves time and effort for users, as they can achieve competitive performance with default parameters. Built-in methods for Handling missing values: Unlike other Models, CatBoost can handle missing values in the input data without requiring imputation. Automatic feature scaling: CatBoost internal scales all the columns to the same scaling whereas in other models we need to convert columns extensively. Robust to Overfitting: CatBoost implements a variety of techniques to prevent overfitting, such as robust tree boosting, ordered boosting, and the use of random permutations for feature combinations. These techniques help in building models that generalize well to unseen data. Built-in cross-validation – CatBoost internally applies a cross-validation method to choose the best hyperparameters for the model. Fast and scalable GPU version: CatBoost offers a GPU-accelerated version of its algorithm, allowing users to train models quickly on large datasets. The GPU implementation enhances scalability and performance, especially when dealing with multi-card configurations....

CatBoost Comparison results with other Boosting Algorithm

Default CatBoost Tuned CatBoost Default LightGBM Tuned LightGBM Default XGBoost Tuned XGBoost Default H2O Adult 0.272978 (±0.0004) (+1.20%) 0.269741 (±0.0001) 0.287165 (±0.0000) (+6.46%) 0.276018 (±0.0003) (+2.33%) 0.280087 (±0.0000) (+3.84%) 0.275423 (±0.0002) (+2.11%) Amazon 0.138114 (±0.0004) (+0.29%) 0.137720 (±0.0005) 0.167159 (±0.0000) (+21.38%) 0.163600 (±0.0002) (+18.79%) 0.165365 (±0.0000) (+20.07%) 0.163271 (±0.0001) (+18.55%) Appet 0.071382 (±0.0002) (-0.18%) 0.071511 (±0.0001) 0.074823 (±0.0000) (+4.63%) 0.071795 (±0.0001) (+0.40%) 0.074659 (±0.0000) (+4.40%) 0.071760 (±0.0000) (+0.35%) Click 0.391116 (±0.0001) (+0.05%) 0.390902 (±0.0001) 0.397491 (±0.0000) (+1.69%) 0.396328 (±0.0001) (+1.39%) 0.397638 (±0.0000) (+1.72%) 0.396242 (±0.0000) (+1.37%) Internet 0.220206 (±0.0005) (+5.49%) 0.208748 (±0.0011) 0.236269 (±0.0000) (+13.18%) 0.223154 (±0.0005) (+6.90%) 0.234678 (±0.0000) (+12.42%) 0.225323 (±0.0002) (+7.94%) Kdd98 0.194794 (±0.0001) (+0.06%) 0.194668 (±0.0001) 0.198369 (±0.0000) (+1.90%) 0.195759 (±0.0001) (+0.56%) 0.197949 (±0.0000) (+1.69%) 0.195677 (±0.0000) (+0.52%) Kddchurn 0.231935 (±0.0004) (+0.28%) 0.231289 (±0.0002) 0.235649 (±0.0000) (+1.88%) 0.232049 (±0.0001) (+0.33%) 0.233693 (±0.0000) (+1.04%) 0.233123 (±0.0001) (+0.79%) Kick 0.284912 (±0.0003) (+0.04%) 0.284793 (±0.0002) 0.298774 (±0.0000) (+4.91%) 0.295660 (±0.0000) (+3.82%) 0.298161 (±0.0000) (+4.69%) 0.294647 (±0.0000) (+3.46%) Upsel 0.166742 (±0.0002) (+0.37%) 0.166128 (±0.0002) 0.171071 (±0.0000) (+2.98%) 0.166818 (±0.0000) (+0.42%) 0.168732 (±0.0000) (+1.57%) 0.166322 (±0.0001) (+0.12%)...

Prerequisites to start Catboost

Prerequisites Supervised Machine Learning Ensemble Learning Gradient Boosting Tree Based Machine Learning...

CatBoost Installation

CatBoost is an open-source library that does not comes pre-installed with Python, so before using CatBoost we must install it in our local system....

Difference between CatBoost, LightGBM and XGboost

The difference between the CatBoost, LightGBM and XGboost are as follows:...

Limitations of CatBoost

Despite of the various features or advantages of catboost, it has the following limitations:...

Conclusions

CatBoost offers a powerful solution for handling categorical features in boosting models, eliminating the need for preprocessing techniques like one-hot encoding. Its efficient handling of categorical variables and built-in methods for missing value handling make it a robust choice for regression, classification, and ranking tasks. With features such as automatic feature scaling, built-in cross-validation, and fast GPU training, CatBoost excels in providing accurate and scalable solutions. Despite its advantages, users should be aware of its limitations, including memory consumption and training time. Continued community support and documentation enhancements can further enhance its usability and effectiveness....

Frequently Asked Questions on CatBoost

Q. What is the principle of CatBoost?...