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CatBoost: An essential Machine Learning tool

- Reading Time: 3 minutes
Elevate your machine learning endeavors with CatBoost, an essential tool for enhanced model performance. Uncover the strengths of this gradient boosting algorithm, designed for categorical feature support, faster training, and superior predictive accuracy. Explore how CatBoost can be a game-changer in your machine learning toolkit.

Since 2017, CatBoost has completed the panel of existing machine learning tools. Fast, efficient and accurate, CatBoost is one of the leading technologies in the field of gradient boosting. In this article, we explain everything you need to know about this technology: applications, benefits, how it works.

What is CatBoost?

CatBoost is an open source algorithm using Machine Learning. It was developed by Yandex, a Russian company. The company had originally developed MatrixNet. A gradient booster library designed by Andrey Gulin to rank search results. Gradually, the project evolved under the guidance of Anna Veronika Dorogush to give rise to CatBoost in 2017.

An algorithm based on gradient boosting

CatBoost is based on gradient boosting. This is a technique that promotes learning, even in the presence of data from different sources. The idea is to transform weak learners into strong ones. To achieve this, new models build on old ones, improving them and reducing errors. Each decision tree is then an evolution of an initial data set.

The algorithm learns and improves to make better decisions.

As a technology using gradient boosting on the decision tree, CatBoost is complementary to Deep Learning. That said, this tool is easier to use. In fact, Deep Learning often works with homogeneous data, especially sensory data such as images or sounds. CatBoost, on the other hand, can work with heterogeneous data and produce accurate predictions.

This is not necessarily the case with many Machine Learning tools, most of which use numerical data.

A wide range of applications

CatBoost can be used for a multitude of applications, such as recommender systems, personal assistants (with voice recognition), self-driving cars, weather forecasts and more.

To realize these different models, CatBoost needs several data sources. For example, for weather forecasts, the algorithm uses historical weather data, information from weather stations, radar measurements and weather models.

This ability to learn and process disparate data means that CatBoost can be used for all kinds of industries.

What are the advantages of this algorithm?

Today, CatBoost is one of the most powerful Machine Learning tools available. There are several reasons for this:

  • High quality without parameter adjustment: CatBoost’s default settings are more than sufficient to provide data experts with optimum quality. So they don’t need to waste time adjusting the various settings.
  • Categorical data processing: in addition to processing numerical data, CatBoost can also process other non-numerical data, such as textual data, colors, etc., so that data scientists don’t have to worry about the quality of their data. This means that data scientists don’t have to turn data into numbers. This means they can exploit multi-format data without affecting CatBoost’s learning.
  • A fast, scalable GPU version: the CatBoost gradient-boosting algorithm is implemented very quickly on the graphics processing unit (GPU). In fact, it is 7 times faster on the GPU than on the CPU (the computer’s central processor).
  • Optimum accuracy: CatBoost produces models with optimum accuracy.
  • Fast predictions: unlike other machine learning tools, you don’t need to run several trials to get excellent results. CatBoost delivers optimal models from the very first run.

Catboost therefore offers Data Scientists a Machine Learning tool that is both easy to use and ultra-efficient.

How does CatBoost work?

In terms of installation, CatBoost can be integrated on Linux, Windows and macOS. Above all, it can be used with Python or R.

Catboost is also compatible with other Machine Learning-based frameworks, such as Tensorflow.

That said, we’ll have to look at the specifics of how CatBoost can be used. The good news is that the algorithm is very easy to learn.

And with a good reason: CatBoost supports One-Hot encoding for categorical data processing. So there’s no need to transform non-numerical data into numbers. However, it is essential to specify the categorical columns (using the cat_features vector). Otherwise, they risk being treated as numerical data.
You’ll also need to prepare the data, and in particular the NAs (empty or null). But that’s just like with any other Machine Learning tool.

To learn more about how CatBoost works, it’s best to take a specialized training course. Like our Data Scientist training course, which will enable you to master the various Machine Learning tools.

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