influence_boosting
Influence finder
This repository implements methods to find influential training samples in Gradient Boosted Decision Trees ensembles
Supporting code for the paper "Finding Influential Training Samples for Gradient Boosted Decision Trees"
67 stars
7 watching
18 forks
Language: Python
last commit: 6 months ago
Linked from 1 awesome list
catboostgradient-boostinginfluence-functionsmachine-learningmachine-learning-algorithmspaperpython
benedekrozemberczki/awesome-decision-tree-papersRelated projects:
| Repository | Description | Stars |
|---|---|---|
 jinlow/forust | A package implementing a lightweight gradient boosted decision tree algorithm | 67 |
 stanfordmlgroup/ngboost | A Python library implementing a machine learning boosting framework with probabilistic prediction capabilities | 1,654 |
 springdaisy/gbdt | An implementation of Gradient Boosted Decision Trees with sparse output for high-dimensional data | 0 |
 serengil/chefboost | A Python library providing a lightweight framework for building decision trees with categorical feature support | 460 |
 kingfengji/mgbdt | An implementation of a gradient boosting decision tree algorithm with target propagation capabilities | 102 |
 charliermarsh/online_boosting | A suite of algorithms and weak learners for the online learning setting in machine learning | 63 |
 harshakokel/kigb | An open-source software framework that integrates human advice into gradient boosting decision trees for improved performance in machine learning tasks. | 8 |
 gbdt-pl/gbdt-pl | An implementation of a gradient boosting algorithm with piece-wise linear regression trees for efficient machine learning model training | 149 |
 arogozhnikov/infiniteboost | A software package implementing an ensemble boosting method with gradient descent | 184 |
 ratschlab/boosting-bbvi | An implementation of Black Box Variational Inference techniques in Python | 8 |
 google-research/noisystudent | A semi-supervised learning method to improve the accuracy of machine learning models by using noisy teacher models and student models. | 753 |
 chasedehan/boostaroota | An algorithm for fast feature selection using XGBoost and other tree-based classifiers | 219 |
 uber-research/learning-to-reweight-examples | Project implementing a method to improve deep learning model robustness by re-weighting examples with noisy labels | 269 |
 max-andr/provably-robust-boosting | Provides provably robust machine learning models against adversarial attacks | 50 |
 dr-darryl-wright/noisy-labels-with-bootstrapping | An implementation of training deep neural networks on noisy labels with bootstrapping using Keras | 22 |