A game theoretic approach to explain the output of any machine learning model.
- interpretability
- machine-learning
- deep-learning
- gradient-boosting
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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.
- gbdt
- gbm
- machine-learning
- data-mining
Machine learning, in numpy
- machine-learning
- neural-networks
- topic-modeling
- gaussian-mixture-models
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
- machine-learning
- decision-trees
- gradient-boosting
- gbm
Fit interpretable models. Explain blackbox machine learning.
- machine-learning
- interpretability
- gradient-boosting
- blackbox
Natural Gradient Boosting for Probabilistic Prediction
- machine-learning
- gradient-boosting
- natural-gradients
- uncertainty-estimation
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
- python
- machine-learning
- random-forest
- tensorflow
Ruby Scoring API for PMML
- ruby-gem
- pmml
- random-forest
- classification
CatBoost a fast, scalable, high performance Gradient Boosting on Decision Trees library. Golang using Cgo for blazing fast inference CatBoost Model 🚀
- cgo
- golang
- deep-learning
- gradient-boosting