Installation
- Overview
- Python package installation
- CatBoost for Apache Spark installation
- R package installation
- Command-line version binary
Key Features
Training parameters
Python package
CatBoost for Apache Spark
- Overview
- Quick start
  - Scala
  - Python
- Spark cluster configuration
- API documentation
- Known limitations
- Usage examples
  - Scala
  - Python
R package
Command-line version
Applying models
- Overview
- C/C++
- Java
  - Overview
  - CatBoostModel
  - CatBoostPredictions
- CoreML
- ONNX
- Rust
- .NET
- Applying the model in ClickHouse
- PMML
- Models exported as code
  - C++
  - Python
Objectives and metrics
Model analysis
Data format description
- Input data
- Output data
Parameter tuning
Speeding up the training
Data visualization
Algorithm details
FAQ
Educational materials
Development and contributions
Contacts

Videos

High level overview of the library
Ordered boosting and categorical features processing in CatBoost short overview
Ordered boosting and categorical features processing in CatBoost full overview
CatBoost on GPU
Tutorial on using CatBoost in Python
Finding Influential Training Samples for Gradient Boosted Decision Trees (on Russian language only)
A Unified Approach to Interpreting Model Predictions
Apache Spark: Introducing CatBoost
Apache Spark: Architecture

High level overview of the library

Ordered boosting and categorical features processing in CatBoost short overview

Ordered boosting and categorical features processing in CatBoost full overview

CatBoost on GPU

Tutorial on using CatBoost in Python

Finding Influential Training Samples for Gradient Boosted Decision Trees (on Russian language only)

A Unified Approach to Interpreting Model Predictions

Apache Spark: Introducing CatBoost

Apache Spark: Architecture

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