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Based on our record, CatBoost seems to be more popular. It has been mentiond 4 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
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 3 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 3 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 3 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 4 years ago
PyCaret - open source, low-code machine learning library in Python
TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.
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PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...
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Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.