Decision Tree Classifier and Boosted Random Forest
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Updated
May 30, 2020 - C++
Decision Tree Classifier and Boosted Random Forest
Gradient boosting is an ensemble machine learning algorithm that combines multiple weak learners to create a strong predictive model.
Conceptual basis of creating enemy learning behavior using gradient boosted regression models to make an effective counter choice.
microGBT is a minimalistic Gradient Boosting Trees implementation
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-label Classification Rules
BLOCKSET: Efficient out of core tree ensemble inference
A powerful tree-based uplift modeling system.
Train Gradient Boosting models that are both high-performance *and* Fair!
A memory efficient GBDT on adaptive distributions. Much faster than LightGBM with higher accuracy. Implicit merge operation.
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
Real time eye tracking for embedded and mobile devices.
Fit interpretable models. Explain blackbox machine learning.
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
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