CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression, classification, and ranking tasks. It functions as a high-performance framework that provides a categorical data processor for transforming non-numeric features, a distributed trainer for large-scale datasets, and GPU acceleration to speed up model construction. The library distinguishes itself through native handling of categorical data and text features, removing the need for manual encoding. It includes a specialized model interpretability tool that leverages SHAP values and featu
This project is a collection of interactive Python notebooks and educational resources designed for mastering data science, machine learning, and numerical computing. It provides a series of practical guides and tutorials covering deep learning, big data processing, and statistical analysis. The repository features specialized instructional suites for implementing classical machine learning algorithms, building deep learning model architectures, and managing AWS cloud infrastructure. It includes dedicated notebooks for data visualization and numerical computing exercises. The project covers
https://github.com/dmlc/xgboost
The main features of tqchen/xgboost are: Machine Learning, Machine Learning Frameworks.
Open-source alternatives to tqchen/xgboost include: yoshoku/rumale — Rumale is a machine learning library in Ruby. febeling/rb-libsvm — Ruby language bindings for LIBSVM. catboost/catboost — CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression,… pkmital/tensorflow_tutorials — This project is a collection of educational Jupyter Notebooks providing tutorials on neural network construction and… donnemartin/data-science-ipython-notebooks — This project is a collection of interactive Python notebooks and educational resources designed for mastering data… albertsuarez/searchly — 🎶 Song similarity search API based on lyrics.
