Scikit Learn
Showing 36 of 115 projects
A Python library that automates the tedious parts of exploratory data analysis with cleaning, feature engineering, visualization, and versioning.
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.
SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments.
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.
An automated cell type annotation tool for single-cell RNA-seq data using logistic regression classifiers.
A scikit-learn compatible classifier that produces human-interpretable decision rules instead of black box models.
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.
Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.
A Python toolbox for auditing machine learning models to detect and quantify bias in black-box predictions.
Predicts Bitcoin price trends using an LSTM-RNN with technical indicators for automated trading via the Binance API.
An intelligent data search and enrichment library for machine learning that automatically finds and adds relevant external features to ML pipelines.
A Python package for exploring, modeling, and visualizing neighborhood and regional change over time using geospatial data.
A joblib backend that enables Python parallel computing tasks to run on Apache Spark clusters.
Python for chemoinformatics
A Python library for stacked generalization (ensemble learning) that supports scikit-learn, XGBoost, and Keras models with out-of-fold prediction saving.
A Python machine learning and informatics suite for analyzing, mining, and modeling chemical and materials data.
A machine learning tool for quantitative risk analysis of Android apps by analyzing declared and actual permission usage.
A Python implementation of individual conditional expectation plots for visualizing machine learning model predictions.
Interactive topic model visualization and interpretation library for Python, compatible with sklearn, Gensim, BERTopic, and Turftopic.
A Python library implementing fairness-aware machine learning algorithms for measuring and mitigating discrimination in predictive models.
A toolkit for evaluating and monitoring machine learning models in clinical healthcare settings.
Provides SigOpt wrappers for scikit-learn to optimize hyperparameters and facilitate model selection.
A Python library providing SigOpt hyperparameter optimization wrappers for scikit-learn and XGBoost models.
A feature engineering wrapper for scikit-learn that uses genetic programming to find optimal feature transformations for machine learning models.
Composable kernels for scikit-learn implemented in JAX, enabling faster kernel computations and automatic differentiation.
OCaml bindings for Python's scikit-learn machine learning library.
Benchmarks comparing machine learning inference performance for tabular models in Go, focusing on low-latency single-sample predictions.
Universal 1D/2D data containers with sklearn-compatible transformers for heterogeneous tabular data analysis.
A scikit-learn pipeline implementing the projection layer of Self-Governing Neural Networks (SGNN) using character n-grams and random hashing.
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