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Everything about CatBoost
Open-source software library developed by Yandex
CatBoost is an open-source software library developed by Yandex. It provides a gradient boosting framework which, among other features, attempts to solve for categorical features using a permutation-driven alternative to the classical algorithm. It works on Linux, Windows, macOS, and is available in Python, R, and models built using CatBoost can be used for predictions in C++, Java, C#, Rust, Core ML, ONNX, and PMML. The source code is licensed under Apache License and available on GitHub. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: CatBoostA 5-minute orientation
- Skim a recent paper: Ensemble Machine Learning of Gradient Boosting (XGBoost, LightGBM, CatBoost) and Attention-Based CNN-LSTM for Harmful Algal Blooms ForecastingInfluential work since 2022
- Read the landmark paper: CatBoost for big data: an interdisciplinary reviewCited 1,787 times
- Go sideways: LightGBMA closely related topic
- Go sideways: XGBoostA closely related topic
📖 Definitions
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🗂️ The subject at a glance
🏆 Landmark papers (most cited)
CatBoost for big data: an interdisciplinary review
Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions
CatBoost model and artificial intelligence techniques for corporate failure prediction
Enhanced short-term load forecasting with hybrid machine learning models: CatBoost and XGBoost approaches
CatBoost: A new approach for estimating daily reference crop evapotranspiration in arid and semi-arid regions of Northern China
Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass
A novel feature engineered-CatBoost-based supervised machine learning framework for electricity theft detection
Comparison of the CatBoost Classifier with other Machine Learning Methods
🆕 Influential since 2022
Ensemble Machine Learning of Gradient Boosting (XGBoost, LightGBM, CatBoost) and Attention-Based CNN-LSTM for Harmful Algal Blooms Forecasting
Daily Streamflow Forecasting in Mountainous Catchment Using XGBoost, LightGBM and CatBoost
Predicting occurrence of liquefaction-induced lateral spreading using gradient boosting algorithms integrated with particle swarm optimization: PSO-XGBoost, PSO-LightGBM, and PSO-CatBoost
Real-time hard-rock tunnel prediction model for rock mass classification using CatBoost integrated with Sequential Model-Based Optimization
Data-driven PSO-CatBoost machine learning model to predict the compressive strength of CFRP- confined circular concrete specimens
📚 Books worth reading
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🔗 Related topics
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