CatBoost Library

A CatBoost Library is a gradient boosting library.

• Context:
  • It can handle categorical features effectively, automatically converting them into numerical formats.
  • It can reduce overfitting with techniques like symmetric decision trees and ordered boosting, especially beneficial for small or noisy datasets.
  • It can provide fast and accurate predictions, even on large and complex datasets, and often outperforms competitors like XGBoost and LightGBM in certain scenarios.
  • It can scale on large datasets and supports distributed training across multiple machines and GPUs.
  • It can be used for a variety of machine learning tasks, including classification, regression, and ranking.
  • ...
• Example(s):
  • CatBoost, v1.2.2 (~2023-09-19) [1].
  • https://github.com/catboost/catboost/releases.
  • ...
• See: XGBoost.

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REferences

2024

• GPT-4
  • NOTES: CatBoost is a machine learning algorithm that excels in gradient boosting on decision trees. It's recognized for its high performance and effectiveness, especially in handling categorical features, and is suitable for various tasks like ranking, classification, regression, and other machine learning applications. Here are some key aspects of CatBoost:
    1. . **How It Works**: CatBoost employs techniques like feature engineering, decision tree optimization, and a novel algorithm called ordered boosting. It focuses on the regions of feature space with the most significant impact on the loss function, leading to more accurate predictions. Additionally, it includes an overfitting detector to improve model generalization and robustness .
    2. . **Features**: CatBoost stands out due to its native handling of categorical features, fast GPU training, visualizations and tools for model and feature analysis, use of Oblivious Trees for faster execution, and ordered boosting to combat overfitting .
    3. . **Applications**: It's widely used in recommendation systems, fraud detection, image and text classification, predicting customer churn, medical diagnoses, and natural language processing. Its ability to handle diverse types of data makes it versatile for various applications [2].
    4. . **Development and Availability**: Developed by Yandex, CatBoost was open-sourced in July 2017 and is under active development. It supports Python, R, Java, C++, and computation on both CPU and GPU .
    5. . **Educational Materials and Resources**: The official [CatBoost website](https://catboost.ai/docs/) provides a comprehensive collection of resources, including documentation on installation, key features, training parameters, model analysis, and educational materials like tutorials and reference papers .
  • CatBoost is known for its superior quality compared to other gradient boosting libraries and is especially effective for datasets with categorical features, offering fast and accurate predictions even on large and complex datasets.

2018

• (Prokhorenkova, et al., 2018) ⇒ Andrey Gulin, Anna Veronika Dorogush, Aleksandr Vorobev, Gleb Gusev, and Liudmila Prokhorenkova. (2018). “CatBoost: Unbiased Boosting with Categorical Features.” In: Proceedings of advances in neural information processing systems 31 (2018).

2017

• https://tech.yandex.com/CatBoost/
  • QUOTE: CatBoost is a state-of-the-art open-source gradient boosting on decision trees library.

    Developed by Yandex researchers and engineers, it is the successor of the MatrixNet algorithm that is widely used within the company for ranking tasks, forecasting and making recommendations. It is universal and can be applied across a wide range of areas and to a variety of problems.

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