CatBoost is another popular and high-performance open-source implementation of the Gradient Boosting Decision Tree (GBDT). To learn how to use this algorithm, please see example notebooks for Classification and Regression. - Source: dev.to / almost 2 years ago

Here are our benchmarks on training time comparing Tangram's Gradient Boosted Decision Tree Library to LightGBM, XGBoost, CatBoost, and sklearn. - Source: dev.to / over 2 years ago

Catboost - CatBoost is an open-source software library developed by Yandex. It provides a gradient boosting framework which attempts to solve for Categorical features using a permutation driven alternative compared to the classical algorithm. Link - https://catboost.ai/. - Source: dev.to / over 2 years ago

CatBoost is an open source algorithm based on gradient boosted decision trees. It supports numerical, categorical and text features. Check out the docs. - Source: dev.to / about 3 years ago

There are several no-code AI websites that you can use like Amazon SageMaker, Apple CreateML or Google AutoML. Source: about 1 year ago

GCP, on the other hand, offers two top options: Google Cloud AutoML, for beginners, and Google Cloud Machine Learning Engine, for handling tasking projects. GCP also provides Tenserflow and Vertex AI complicated machine learning abilities. - Source: dev.to / over 1 year ago

Just outsource the work to Google or Amazon. Source: over 2 years ago

We can also note the appearance of Machine Learning, creating dynamic processes over data that would have been tedious to analyse, either by hand or through specific code. This enables writing potentially complex behaviours with a few lines of code in some cases. Even then, there is some automation of it to the point where you only have to provide data to get working results. - Source: dev.to / almost 3 years ago

You might want to check out automl Google AutoML. Source: almost 3 years ago