Tags / Machine learning /
Classical machine learning library
A library of non-deep-learning algorithms such as regression, trees, gradient boosting, or clustering.
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description:
scikit-learn: machine learning in Python
- dmlc/xgboost29K
readme:
XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.
readme:
LightGBM is a gradient boosting framework that uses tree based learning algorithms.
readme:
- Decision trees (CART) - [Bagging] Random forests - [Boosting] Gradient-boosted decision trees
readme:
Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.
- apple/turicreatearchived · 11K
readme:
| Regression | Predict numeric values | | Clustering | Group similar datapoints together|
readme:
GoLearn implements the scikit-learn interface of Fit/Predict, so you can easily swap out estimators for trial and error.
readme:
With ML.NET, you can train models for a variety of scenarios, like classification, forecasting, and anomaly detection.
readme:
CatBoost is a machine learning method based on gradient boosting over decision trees.
- clips/pattern8.9K
readme:
* Machine Learning: vector space model, clustering, classification (KNN, SVM, Perceptron)
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