scikit-learn: machine learning in Python
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Open standard for machine learning interoperability
Fast and Accurate ML in 3 Lines of Code
Open-source, low-code AutoML platform for Python. PyCaret 4.0: sklearn-native engine + React control plane.
- data-science
- python
- machine-learning
- pycaret
Automated Machine Learning with scikit-learn
- automl
- scikit-learn
- automated-machine-learning
- hyperparameter-optimization
An open source python library for automated feature engineering
- feature-engineering
- machine-learning
- data-science
- automated-machine-learning
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
Seamlessly integrate LLMs into scikit-learn.
Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
- machine-learning
- scikit-learn
- statistical-learning
- xgboost
A modular active learning framework for Python
- scikit-learn
- machine-learning
- python
- active-learning
Feature engineering and selection open-source Python library compatible with sklearn.
Genetic Programming in Python, with a scikit-learn inspired API
- genetic-programming
- machine-learning
- python
- scikit-learn
Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models
- forecasting
- scikit-learn
- machine-learning
- python
Metric learning algorithms in Python
- python
- metric-learning
- scikit-learn
- machine-learning
Extra blocks for scikit-learn pipelines.
- scikit-learn
- machine-learning
- common-sense
Transpile trained scikit-learn estimators and transformers to C, C#, Go, Java, JavaScript, PHP and Ruby.
PySpark + Scikit-learn = Sparkit-learn
A scikit-learn based module for multi-label et. al. classification
- classification
- scikit
- scikit-learn
- multi-label
Combines the ease of use of scikit-learn with the power of Theano/Lasagne
The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.
- pipeline
- pipeline-framework
- machine-learning
- deep-learning
Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.
A set of tools for creating and testing machine learning features, with a scikit-learn compatible API
Stacked Generalization (Ensemble Learning)
- stacking
- ensemble-learning
- scikit-learn
- xgboost
Python package implementing ML feature engineering and pre-processing for polars or pandas dataframes.
- feature-engineering
- transformers
- pre-processing
- data-science