New ML algorithms are becoming more popular as Data Science contests become more prominent, particularly on the site Kaggle. Whereas XGBoost was the most competitive and accurate algorithm, for the most part, CatBoost has emerged as the new leader. The business Yandex has released an open-source library based on gradient-boosting decision trees. They incorporate GitHub references and examples, news, benchmarks, comments, contacts, tutorials, and installation in their guide. CatBoost documentation has benchmarked and proven that XGBoost, LightGBM, and H2O are the best with both tweaked and default results. CatBoost, on the other hand, is the way to go if you have a lot of category variables. If you want to discover more about Yandex’s fantastic library, continue reading below.
- CatBoost is recommended because it is simple to use, efficient, and works particularly well with categorical data.
CatBoost stands for ‘categorical’ boosting, as the name suggests. It’s faster to use than XGBoost, for example, since it doesn’t involve pre-processing your data, which might take the longest time in a conventional Data Science model building process. Another issue with other methods is that when they use categorical variables such as IDs, they produce an impossible to compute matrix with hundreds of columns built from dummy variables or one-hot-encoding. CatBoost solves this challenge by transforming category data in a unique method.
CatBoost is based on gradient boosted decision trees that use a training dataset and a validation dataset to measure accuracy. Those decision trees are created sequentially during training, with each tree’s loss minimized.
When selecting the optimum ways to partition data into buckets, quantization is employed for the numerical features based on CatBoost’s initial settings.
- Implementation– CatBoost has user-friendly interfaces. With scikit-learn, R, and command-line interfaces, the CatBoost algorithm may be utilized in Python.
CatBoost’s GPU version is fast and scalable, allowing researchers and machine learning developers at Yandex to work on data sets with tens of thousands of objects without lagging.
When opposed to training on the CPU, training on the GPU provides you with a faster result. To top it off, the faster the dataset becomes, the more substantial the speedup. The multi-card arrangement is easily supported by CatBoost. Use a multi-card arrangement for huge datasets.
- Predictions and faster training– The maximum number of GPUs per server before server improvements was eight. Although some data sets are larger, CatBoost makes advantage of distributed GPUs.
CatBoost can learn and generate predictions 15 times quicker than other algorithms because of this feature.
- Community– The lack of a staff to contact when you have a problem with a product you use may be quite aggravating. In the case of CatBoost, however, this is not the case.
The CatBoost community is expanding, and the creators are looking for comments and suggestions. A Slack community, a Telegram channel, and Stack Overflow help are all available. If you ever find an issue, there is a bug report page on GitHub.
Here are some situations where utilizing CatBoost is ideal:
- On a solid data set, a short training period is required- CatBoost, unlike some other machine learning algorithms, works effectively with a limited amount of data. Overfitting, on the other hand, should be avoided. It’s possible that some tweaking of the settings is required here.
- Working with a tiny collection of data- This is one of the CatBoost algorithm’s major advantages. Assume your data collection contains categorical elements, and transforming it to numerical representation appears to be a significant undertaking. In that instance, you may use CatBoost’s strength to simplify the process of creating your model.
- Categorical datasets- CatBoost is much quicker than many other machine learning methods when working with these types of datasets. Splitting, tree structure, and training are all tuned for GPU and CPU performance.
CatBoost provides several helpful features that are simple to apply. The default settings produce excellent results even without parameter adjustment, categorical features do not require preprocessing, rapid calculation, increased accuracy with minimal overfitting, and efficient predictions are some of the primary benefits of this competitive library. Yandex researchers have created an exceptionally helpful library that can be used in a variety of competition, career, and production scenarios. They’ve also demonstrated that, when compared to LightGBM, their benchmark quality is superior on some prominent datasets.