Popular GitHub Repositories for Catboost

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

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Machine Learning University: Decision Trees and Ensemble Methods

R package for automation of machine learning, forecasting, model evaluation, and model interpretation

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A 100%-Julia implementation of Gradient-Boosting Regression Tree algorithms

Automatic machine learning for tabular data. ???

AutoFlow : Automatic machine learning workflow modeling platform

Analyzing the HR Criteria of a Company and how they promote their Employees and keep Balance between them using Data Analytics, Data Visualizations, and Machine Learning Models for Classification Purposes.

Easy Custom Losses for Tree Boosters using Pytorch

A binary classification model is developed to predict the probability of paying back a loan by an applicant. Customer previous loan journey was used to extract useful features using different strategies such as manual and automated feature engineering, and deep learning (CNN, RNN). Various machine learning algorithms such as Boosted algorithms (XGBoost, LightGBM, CatBoost) and Deep Neural Network are used to develop a binary classifier and their performances were compared.

Nested Cross-Validation for Bayesian Optimized Gradient Boosting

Predicting Fuel Load from earth observation data using Machine Learning

Automation of feature engineering, machine learning, model evaluation, model interpretation, eda, forecasting, recommender systems and more.

Predict salary for any job with machine learning

A package to build Gradient boosted trees for ordinal labels

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CatBoost a fast, scalable, high performance Gradient Boosting on Decision Trees library. Golang using Cgo for blazing fast inference CatBoost Model ?

Harness the power of machine learning to forecast rice and wheat crop yields per acre in India, aiming to empower smallholder farmers, combat poverty and malnutrition, utilizing data from Digital Green surveys to revolutionize agriculture and promote sustainable practices in the face of climate change for enhanced global food security.

Fraud detection using lgb, catboost, rf, etc