Ensemble Learning
Showing 21 of 21 projects
An automated machine learning library that trains and deploys high-accuracy models for tabular, text, image, and time series data with minimal code.
An automated machine learning toolkit that serves as a drop-in replacement for scikit-learn estimators.
An open-source, in-memory platform for distributed and scalable machine learning with support for a wide range of algorithms and big data technologies.
A Python library providing extensions and utilities for data science and machine learning tasks.
An Automated Machine Learning Python package for tabular data with feature engineering, hyperparameter tuning, explanations, and automatic documentation.
A Ruby library implementing the ID3 algorithm for decision tree learning with support for continuous and discrete datasets.
A web-based tool for automated hyperparameter tuning and stacked ensemble creation in Python.
A Python library for building high-performance, memory-efficient ensemble learning networks with a Scikit-learn compatible API.
A Python package for stacking (stacked generalization) with both functional and scikit-learn compatible APIs.
A Python library for class-imbalanced ensemble learning with 30+ algorithms, built on scikit-learn.
A Scala framework for distributed supervised learning of decision tree ensemble models, inspired by Google's PLANET.
An R package for automatic optimal predictor ensembling via cross-validation with dozens of machine learning algorithms.
A Python library for stacked generalization (ensemble learning) that supports scikit-learn, XGBoost, and Keras models with out-of-fold prediction saving.
A PyTorch-based Python package for deep and machine learning analysis of microscopy data, designed for domain scientists.
A Python library for evaluating binary classifiers in machine learning ensembles using Shapley value computation and approximation methods.
An open-source starter solution for the Kaggle Toxic Comment Classification Challenge, providing ready-to-use machine learning pipelines for detecting online harassment.
A parallel Random Forest implementation in Go for classification and regression tasks.
A Python library providing SigOpt hyperparameter optimization wrappers for scikit-learn and XGBoost models.
A comprehensive Random Forest implementation in Go with extensions for deep forests, continuous learning, feature selection, and anomaly detection.
A Julia package implementing Decision Tree (CART) and Random Forest algorithms for classification and regression tasks.
Compares stacked ensemble learning against individual ML algorithms for stock market trend prediction using Python.
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