Pinned Repositories
awesome-interpretable-machine-learning
Feature-Selection
Features selector based on the self selected-algorithm, loss function and validation method
Feature-Selection-for-Machine-Learning
Methods with examples for Feature Selection during Pre-processing in Machine Learning.
hpelm
High performance implementation of Extreme Learning Machines (fast randomized neural networks).
hughyw
new here
interpreting-decision-trees-and-random-forests
Unwrapping decision trees and random forests to make them less of a black box
lofo-importance
Leave One Feature Out Importance
ML-NLP
此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。
PDPbox
python partial dependence plot toolbox
random-forest-importances
Code to compute permutation and drop-column importances in Python scikit-learn random forests
hughstime's Repositories
hughstime/awesome-interpretable-machine-learning
hughstime/Feature-Selection
Features selector based on the self selected-algorithm, loss function and validation method
hughstime/Feature-Selection-for-Machine-Learning
Methods with examples for Feature Selection during Pre-processing in Machine Learning.
hughstime/hpelm
High performance implementation of Extreme Learning Machines (fast randomized neural networks).
hughstime/hughyw
new here
hughstime/interpreting-decision-trees-and-random-forests
Unwrapping decision trees and random forests to make them less of a black box
hughstime/lofo-importance
Leave One Feature Out Importance
hughstime/ML-NLP
此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。
hughstime/PDPbox
python partial dependence plot toolbox
hughstime/random-forest-importances
Code to compute permutation and drop-column importances in Python scikit-learn random forests
hughstime/RandomForest-feature-importance
Explore Importance of Features in Random Forests
hughstime/unbiased-feature-importance
Implementation of unbiased measurement of feature importance in Random Forests