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slundberg / shap

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A game theoretic approach to explain the output of any machine learning model.

machine-learning deep-learning gradient-boosting interpretability shapley shap explainability

Updated Mar 30, 2022
Jupyter Notebook

microsoft / LightGBM

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Open

Summary

mypy shows some issues in LightGBM's Python package.

mypy \
    --exclude='python-package/compile/|python-package/build' \
    --ignore-missing-imports \
    python-package/

18 errors in 4 files (click me)

python-package/lightgbm/compat.py:12: error: Name 'Series' already defined (possibly by an import)
python-package

help wanted good first issue

Open

List of dependencies that should be updated from time to time

6

Open

[python-package] type hints in python package

9

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ddbourgin / numpy-ml

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Machine learning, in numpy

machine-learning reinforcement-learning word2vec lstm neural-networks gaussian-mixture-models vae topic-modeling attention resnet bayesian-inference wavenet mfcc knn gaussian-processes hidden-markov-models gradient-boosting wgan-gp good-turing-smoothing

Updated Jan 8, 2022
Python

EpistasisLab / tpot

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A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

python data-science machine-learning automation random-forest scikit-learn model-selection xgboost hyperparameter-optimization feature-engineering automl gradient-boosting automated-machine-learning parameter-tuning

Updated Mar 3, 2022
Python

catboost / catboost

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Open

More concise error message with feature name, not only feature_idx

fingoldo commented Mar 24, 2022

Problem:

_catboost.pyx in _catboost._set_features_order_data_pd_data_frame()

_catboost.pyx in _catboost.get_cat_factor_bytes_representation()

CatBoostError: Invalid type for cat_feature[non-default value idx=1,feature_idx=336]=2.0 : cat_features must be integer or string, real number values and NaN values should be converted to string.

Could you also print a feature name, not o

good first issue

Open

GPU accelerated training not available during multitasking and model retraining

8

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interpretml / interpret

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Open

Interpret

5

wh3nd1g0h commented Jul 29, 2020

Yes

good first issue

benedekrozemberczki / awesome-decision-tree-papers

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A collection of research papers on decision, classification and regression trees with implementations.

classifier machine-learning random-forest statistical-learning xgboost lightgbm gradient-boosting-machine ensemble-learning cart decision-tree tree-ensemble decision-tree-classifier gradient-boosting classification-trees decision-tree-learning decision-tree-model classification-model catboost regression-tree machine-learning-research

Updated Mar 2, 2022
Python

ClimbsRocks / auto_ml

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[UNMAINTAINED] Automated machine learning for analytics & production

python data-science machine-learning deep-learning analytics tensorflow scikit-learn keras artificial-intelligence xgboost hyperparameter-optimization lightgbm machine-learning-library deeplearning production-ready feature-engineering machine-learning-pipelines automl gradient-boosting automated-machine-learning

Updated Feb 10, 2021
Python

stanfordmlgroup / ngboost

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Open

Add Laplace to tests

ryan-wolbeck commented Nov 7, 2020

Laplace was added to the repo but I think we should add it as well to tests/test_distns.py

enhancement good first issue

Open

Probelms with the output for regression with LogNormal

7

Open

benedekrozemberczki / awesome-fraud-detection-papers

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A curated list of data mining papers about fraud detection.

classifier data-science data-mining deep-learning random-forest credit-card-fraud classification fraud-management logistic-regression fraud-prevention credit-scoring churn link-prediction fraud-detection gradient-boosting fraud-checker graph-classification credit-card-validation credit-card-fraud-detection fraud-explorer

Updated Mar 2, 2022
Python

benedekrozemberczki / awesome-gradient-boosting-papers

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A curated list of gradient boosting research papers with implementations.

classifier machine-learning deep-learning random-forest h2o xgboost lightgbm gradient-boosting-machine adaboost decision-tree gradient-boosting-classifier classification-algorithm gradient-boosting boosting classification-trees xgboost-algorithm catboost gradient-boosted-trees classification-tree gradient-boosting-decision-trees

Updated Mar 2, 2022
Python

sberbank-ai-lab / LightAutoML

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LAMA - automatic model creation framework

Updated Mar 31, 2022
Python

Freemanzxp / GBDT_Simple_Tutorial

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python实现GBDT的回归、二分类以及多分类，将算法流程详情进行展示解读并可视化，庖丁解牛地理解GBDT。Gradient Boosting Decision Trees regression, dichotomy and multi-classification are realized based on python, and the details of algorithm flow are displayed, interpreted and visualized to help readers better understand Gradient Boosting Decision Trees

machine-learning gbdt gradient-boosting gradient-boosting-decision-trees gnm

Updated Jun 15, 2019
Python

zygmuntz / hyperband

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Tuning hyperparams fast with Hyperband

machine-learning hyperparameters hyperparameter-optimization hyperparameter-tuning gradient-boosting-classifier gradient-boosting

Updated Aug 15, 2018
Python

TorchEnsemble-Community / Ensemble-Pytorch

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A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.

deep-learning pytorch neural-networks ensemble ensemble-learning deeplearning gradient-boosting pytorch-tutorial bagging voting-classifier

Updated Jan 16, 2022
Python

tensorflow / decision-forests

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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 keras ml decision-trees gradient-boosting interpretability decision-forest

Updated Mar 31, 2022
Python

elucideye / drishti

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Real time eye tracking for embedded and mobile devices.

android c-plus-plus ios xgboost eye-tracking acf face-detection object-detection dlib hunter face-tracking gradient-boosting drishti

Updated Sep 4, 2019
C++

serengil / chefboost

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A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4,5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting (GBDT, GBRT, GBM), Random Forest and Adaboost w/categorical features support for Python