A scikit-learn compatible neural network library that wraps PyTorch
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Visual analysis and diagnostic tools to facilitate machine learning model selection.
- machine-learning
- visual-analysis
- model-selection
- visualization
🛠 All-in-one web-based IDE specialized for machine learning and data science.
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 library for debugging/inspecting machine learning classifiers and explaining their predictions
Pandas integration with sklearn
Python library for portfolio optimization, factor model construction, and risk management built on scikit-learn
- asset-allocation
- portfolio
- portfolio-optimization
- quantitative-finance
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
An intuitive library to add plotting functionality to scikit-learn objects.
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.
Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
- visualization
- python3
- python
- xgboost
Genetic Programming in Python, with a scikit-learn inspired API
- genetic-programming
- machine-learning
- python
- scikit-learn
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
Python implementations of the Boruta all-relevant feature selection method.
MLeap: Deploy ML Pipelines to Production
- scikit-learn
- spark
- data-pipelines
- transformers
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