CatBoost

What it is

CatBoost is a general-purpose gradient boosting library that supports categorical features out of the box. It offers fast inference and can run on CPU or GPU, including multi-GPU setups.

CatBoost stands out with its native support for categorical features, making it particularly effective in scenarios where feature preprocessing would otherwise be complex or time-consuming.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Limited language support beyond Python and C++

   Official support is primarily for Python and C++, other languages rely on community efforts

2. 02

   Complex setup for GPU usage, especially multi-GPU setups

   Requires specific configurations and dependencies that can be challenging to set up correctly

What it does well

• Supports categorical features out of the box

• Fast inference implementation

• Runs on CPU and GPU (including multi-GPU setups)

• Easy to install and use

• High performance in machine learning tasks

Pricing

Free tier

YesOpen source — free to use

Paid from

$0

Model

Flat rate

Enterprise plan

No

Pricing not independently confirmed — check the vendor’s page.