Scikit Learn
Showing 36 of 86 projects
Automated machine learning library for production and analytics, handling feature engineering, model selection, and hyperparameter optimization.
Hyperopt-sklearn automates hyperparameter optimization and model selection for scikit-learn machine learning pipelines.
Python implementation of the Boruta all-relevant feature selection method with scikit-learn compatibility.
A Python package for concise, transparent, and accurate predictive modeling with sklearn-compatible interpretable models.
An open-source Python repository providing around 40 feature selection algorithms for machine learning applications.
MLeap is a portable execution engine for deploying machine learning pipelines from Spark and Scikit-learn without their runtime dependencies.
A Python library for time series forecasting using scikit-learn compatible machine learning models.
A Python library for time series forecasting using scikit-learn compatible machine learning models.
A free software AI accelerator that speeds up scikit-learn applications by 10-100x on CPUs and GPUs with no code changes.
Implementation of hyperparameter optimization methods for ML/DL models with sample code for regression and classification tasks.
Transpile trained scikit-learn estimators to C, Java, JavaScript, Go, PHP, and Ruby for embedded systems and performance-critical applications.
A web-based tool for automated hyperparameter tuning and stacked ensemble creation in Python.
A Python library implementing Factorization Machines with a scikit-learn compatible API for regression, classification, and ranking tasks.
A scikit-learn compatible Python module for multi-label classification tasks.
A Python library for building high-performance, memory-efficient ensemble learning networks with a Scikit-learn compatible API.
A Python machine learning package for incremental learning on streaming data with concept drift detection.
A collection of IPython notebooks demonstrating data analysis and machine learning techniques on security datasets.
A scikit-learn compatible hyperparameter optimization tool using evolutionary algorithms instead of grid search.
A fast GPU-accelerated library for training Gradient Boosting Decision Trees (GBDT) and Random Forests.
IPython-based environment for reproducible machine learning research with unified wrappers for multiple ML libraries.
A Python package for stacking (stacked generalization) with both functional and scikit-learn compatible APIs.
A fast, robust Python library to detect offensive language in text using a machine learning model.
A Python library that automates the tedious parts of exploratory data analysis with cleaning, feature engineering, visualization, and versioning.
A high-performance C++/DPC++ library for accelerated machine learning on CPUs, GPUs, and distributed systems.
A tutorial series comparing how to implement data science concepts and build applications in both Python and R ecosystems.
A collection of IPython notebooks containing machine learning experiments and examples using scikit-learn and related Python libraries.
A Python library implementing Self-Organizing Maps (SOM) with batch training, PCA initialization, and visualization tools.
Automatically builds high-performance interpretable machine learning models with minimal features using a single line of code.
A comprehensive PhD dissertation providing an in-depth theoretical and practical analysis of random forests, from algorithmic foundations to interpretability.
A Python package providing Bayesian machine learning algorithms with a scikit-learn compatible API.
A scikit-learn compatible classifier that produces human-interpretable decision rules instead of black box models.
An automated cell type annotation tool for single-cell RNA-seq data using logistic regression classifiers.
A pure Go library for making predictions with Gradient Boosting Regression Trees models from LightGBM, XGBoost, and scikit-learn.
Python implementation of the RuleFit algorithm for interpretable machine learning predictions using rule ensembles.
A Python library for class-imbalanced ensemble learning with 30+ algorithms, built on scikit-learn.
A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for machine learning.
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