awesome-repositories.com
Blog
MCP
awesome-repositories.com

Discover the best open-source repositories with AI-powered search.

ExploreCurated searchesOpen-source alternativesSelf-hosted softwareBlogSitemap
ProjectMCP serverAboutHow we rankPress
LegalPrivacyTerms
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
catboost avatar

catboost/catboost

0
View on GitHub↗
8,808 stars·1,263 forks·C++·apache-2.0·48 viewscatboost.ai↗

Catboost

CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression, classification, and ranking tasks. It functions as a high-performance framework that provides a categorical data processor for transforming non-numeric features, a distributed trainer for large-scale datasets, and GPU acceleration to speed up model construction.

The library distinguishes itself through native handling of categorical data and text features, removing the need for manual encoding. It includes a specialized model interpretability tool that leverages SHAP values and feature importance to explain individual predictions and overall model behavior.

The system covers a broad set of capabilities including distributed training via Apache Spark, model evaluation through cross-validation, and hyperparameter optimization. It also provides an interoperable model exporter to transition trained trees into formats such as ONNX, PMML, JSON, and CoreML, as well as mechanisms for deploying models directly into database environments for real-time inference.

Features

  • Gradient Boosting Libraries - Provides a high-performance library for training gradient-boosted decision tree ensembles for regression, classification, and ranking.
  • Gradient Boosting - Implements an iterative ensemble technique to build decision tree models for regression, classification, and ranking.
  • Model Training - Builds decision tree ensembles to solve regression and classification problems by iteratively reducing loss.
  • Class Probability Estimation - Calculates the likelihood that an input belongs to specific target classes through probability estimation.

AI search

Explore more awesome repositories

Describe what you need in plain English — the AI ranks thousands of curated open-source projects by relevance.

Start searching with AI
  • Pretrained Model Integrations - Loads previously trained models into a runtime environment to generate predictions on new datasets.
  • Diverse Feature Type Conversions - Converts categorical, text, and embedding features into numerical representations suitable for decision tree construction.
  • Feature Importance Attribution - Quantifies the relative contribution of different features and training objects to final model predictions.
  • GPU-Accelerated Training - Leverages graphics processing units to accelerate the training of decision tree ensembles on large datasets.
  • Model Interpretability Tools - Includes a diagnostic suite using SHAP values and feature importance to explain model behavior and individual predictions.
  • Model Predictions - Applies trained models to datasets to generate discrete class predictions or numerical values.
  • Model Explainability - Analyzes feature importance and uses SHAP values to explain why a model made a specific prediction.
  • Ranking Model Training - Builds predictive models designed to order lists of items by relevance.
  • Regularization and Sampling - Adjusts boosting types and bootstrap sampling methods to reduce computational expense and increase training speed.
  • SHAP Value Computations - Uses SHAP value computations to quantify the specific contribution of each feature to the model formula.
  • Text-to-Numeric Transformations - Transforms raw text into numerical representations using tokenizers and dictionaries to enable machine learning processing.
  • Training Convergence Optimization - Balances iterations and learning rate to reduce training time while preventing model overfitting.
  • Categorical Encodings - Transforms categorical data into numerical form using one-hot encoding to optimize training speed and quality.
  • Categorical Encoders - Provides a data transformation engine that converts non-numeric and text features into numerical representations.
  • Categorical - Automatically processes non-numeric features during training to remove the need for manual data encoding.
  • Tabular Model Explanation - Calculates feature attribution values to explain which input variables most influenced a particular prediction in tabular datasets.
  • AI-Based Relevance Ranking - Implements specialized loss functions to optimize the relevance ranking of items for search and recommendation.
  • Continuous Model Training - Continues training an existing model by using it as a starting point for further iterations.
  • Data Preprocessing - Uses specialized categorical data types during input preparation to speed up the preprocessing of categorical features.
  • Tree Structure Configurations - Controls decision tree complexity through adjustments to depth, growing policies, and split counts.
  • Spark Integrations - Enables distributing the training and application of gradient boosting models across Apache Spark clusters.
  • Distributed ML Trainers - Scales model construction across a cluster using Apache Spark for massive datasets.
  • Distributed Training - Processes numerical and categorical features across a cluster to scale model training for large datasets.
  • Custom Performance Metrics - Implements user-defined objects to measure model performance during the training process.
  • Feature Contribution Analysis - Calculates and plots target averages across feature buckets to analyze how specific variables influence predictions.
  • Large-Scale Model Training - Distributes the training of predictive models across Spark clusters or multi-GPU environments to process massive datasets.
  • Mathematical Training Objectives - Specifies the loss function to be optimized during training to define the machine learning problem.
  • Custom Loss Functions - Allows the definition of custom mathematical objective functions to minimize during training.
  • Model Exporters - Converts trained models into the XML-based PMML format for sharing and execution within compliant applications.
  • ONNX Model Exporters - Converts trained decision tree ensembles into the standardized ONNX format for cross-engine compatibility.
  • Model Evaluation Metrics - Calculates a wide range of accuracy and error metrics for regression, classification, and ranking tasks.
  • Model Export Formats - Ships utilities for converting trained models into standard formats like ONNX, PMML, JSON, and CoreML for cross-platform deployment.
  • Overfitting Debuggers - Automatically stops training when validation performance degrades to prevent overfitting.
  • Hyperparameter Optimization - Searches for optimal model configurations using grid search, randomized search, and external frameworks.
  • Best Iteration Selection - Identifies and selects the optimal training iteration based on a validation dataset.
  • Model Performance Visualizations - Generates graphical representations of the training process and model behavior to diagnose performance.
  • Numerical Split Optimizations - Optimizes the number of splits for numerical features to increase precision for high-impact variables.
  • Random Subspace Methods - Limits the number of features considered at each split to increase iteration speed for high-dimensional datasets.
  • Result Reproducibility Controls - Provides mechanisms to manage seeds and environment configurations for deterministic model training outcomes.
  • Training Data Sampling Strategies - Uses minimal variance sampling to select training subsets that reduce noise and instability.
  • Training Progress Monitors - Tracks metric values, loss functions, and estimated completion time in real-time during the training process.
  • Dataset Quantization - Saves and reloads pre-quantized numerical datasets to avoid repeating the quantization process across training runs.
  • Encoding Complexity Tuning - Controls one-hot encoding limits and feature combination complexity to reduce time spent on categorical data.
  • Split Precision Reduction - Caps the number of splits considered for numerical features to accelerate training when using a GPU.
  • Embedding Dimension Reduction - Transforms high-dimensional vector data into lower-dimensional numeric features using linear discriminant analysis or nearest neighbor search.
  • Model Export Formats - Exports trained models to ONNX, PMML, or CoreML for high-speed execution in production environments or databases.
  • AI & Machine Learning - High-performance gradient boosting on decision trees
  • Decision Tree Models - Gradient boosting library with categorical support.
  • General Machine Learning - Gradient boosting library with built-in categorical feature support.
  • Gradient Boosting - Fast, scalable gradient boosting library for various machine learning tasks.
  • Gradient Boosting Research - Unbiased gradient boosting with categorical feature support.
  • Machine Learning - Gradient boosting with categorical features support.
  • Machine Learning Frameworks - Gradient boosting library with built-in support for categorical features.
  • Machine Learning Libraries - Gradient boosting library for ranking and regression.
  • Machine Learning Frameworks - Gradient boosting library with native support for categorical features.

    • Star history

    Star history chart for catboost/catboostStar history chart for catboost/catboost

    How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

    Frequently asked questions

    What does catboost/catboost do?

    CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression, classification, and ranking tasks. It functions as a high-performance framework that provides a categorical data processor for transforming non-numeric features, a distributed trainer for large-scale datasets, and GPU acceleration to speed up model construction.

    What are the main features of catboost/catboost?

    The main features of catboost/catboost are: Gradient Boosting Libraries, Gradient Boosting, Model Training, Class Probability Estimation, Pretrained Model Integrations, Diverse Feature Type Conversions, Feature Importance Attribution, GPU-Accelerated Training.

    Which projects share features with catboost/catboost?

    Projects with overlapping indexed features include: lightgbm-org/lightgbm — LightGBM is a gradient boosting framework used to train decision tree ensembles for classification, regression, and… lyhue1991/eat_tensorflow2_in_30_days — This project is a structured learning curriculum and technical reference for mastering deep learning with TensorFlow.… dmlc/xgboost — XGBoost is a distributed machine learning library for implementing scalable gradient boosting decision trees used for… fastai/course-v3 — This repository is a comprehensive educational program and deep learning framework designed to teach practical deep… d2l-ai/d2l-en — This project is an educational platform and research toolkit designed to teach deep learning through a combination of… dotnet/machinelearning — This is a cross-platform framework for building, training, and deploying custom machine learning models within the…

    Projects sharing features with Catboost

    These projects share indexed features with Catboost. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
    • lightgbm-org/lightgbmlightgbm-org avatar

      lightgbm-org/LightGBM

      18,460View on GitHub↗

      LightGBM is a gradient boosting framework used to train decision tree ensembles for classification, regression, and ranking tasks. It functions as a distributed machine learning library and a decision tree ensemble implementation that utilizes leaf-wise growth and histogram-based feature binning. The framework is distinguished by its ability to offload heavy computations to CUDA or OpenCL devices for GPU acceleration and its capacity to parallelize training across multiple nodes using sockets, MPI, or Dask. It includes a specialized categorical feature processor that optimizes partitions for

      C++
      View on GitHub↗18,460
    • lyhue1991/eat_tensorflow2_in_30_dayslyhue1991 avatar

      lyhue1991/eat_tensorflow2_in_30_days

      9,933View on GitHub↗

      This project is a structured learning curriculum and technical reference for mastering deep learning with TensorFlow. It provides a comprehensive guide for building, training, and deploying neural networks, combining theoretical fundamentals with practical implementation examples. The repository distinguishes itself by covering the end-to-end machine learning workflow, from low-level tensor mathematics and linear algebra to the creation of complex model architectures. It includes specific guidance on developing data pipelines for diverse data types, such as images, text, and time-series seque

      Pythontensorflowtensorflow-examplestensorflow-tutorial
      View on GitHub↗9,933
    • dmlc/xgboostdmlc avatar

      dmlc/xgboost

      28,471View on GitHub↗

      XGBoost is a distributed machine learning library for implementing scalable gradient boosting decision trees used for regression, classification, and ranking. It functions as a predictive model framework and a cross-language toolkit, providing a core implementation with native bindings for Python, R, Java, Scala, and C++. The system is designed as a GPU-accelerated library that utilizes CUDA and NCCL to speed up the training of decision tree ensembles. It operates as a distributed framework capable of scaling training and prediction across multi-node clusters and GPU environments to process m

      C++distributed-systemsgbdtgbm
      View on GitHub↗28,471
    • fastai/course-v3fastai avatar

      fastai/course-v3

      4,914View on GitHub↗

      This repository is a comprehensive educational program and deep learning framework designed to teach practical deep learning using PyTorch through notebooks and code examples. It serves as a high-level library for building, training, and deploying neural networks, acting as a model training orchestrator that coordinates PyTorch models, optimizers, and loss functions. The project provides specialized toolkits for computer vision, natural language processing, and tabular data preprocessing. It distinguishes itself through advanced training controls such as discriminative learning rates, a two-w

      Jupyter Notebookdata-sciencedeep-learningfastai
      View on GitHub↗4,914
    Compare all 30 related projects→