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dmlc / xgboost

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Trusted-AI / adversarial-robustness-toolbox

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Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference

python deep-neural-networks attack scikit-learn extraction artificial-intelligence xgboost lightgbm logistic-regression support-vector-machine decision-trees ibm-research poisoning adversarial-machine-learning catboost ibm-research-ai gradient-boosted-trees defense-methods codait trusted-ai
  • Updated Oct 13, 2020
  • Python

BayesWitnesses / m2cgen

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add option to save generated code into file

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StrikerRUS
StrikerRUS commented Oct 18, 2019

I'm sorry if I missed this functionality, but CLI version hasn't it for sure (I saw the related code only in generate_code_examples.py). I guess it will be very useful to eliminate copy-paste phase, especially for large models.

Of course, piping is a solution, but not for development in Jupyter Notebook, for example.

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enhancement good first issue
awesome-decision-tree-papers

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 Aug 2, 2020
  • Python
awesome-gradient-boosting-papers

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 Oct 11, 2020
  • Python
mljar-supervised

mljar / mljar-supervised

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Handle all models with error

pplonski
pplonski commented Sep 30, 2020

The AutoML crashes if all models have error. It should be handled more gently.

The example of crash:

AutoML directory: AutoML_88
The task is multiclass_classification with evaluation metric logloss
AutoML will use algorithms: ['MLP']
AutoML steps: ['simple_algorithms', 'default_algorithms', 'not_so_random', 'hill_climbing_1', 'hill_climbing_2']
Skip simple_algorithms because no parame
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bug good first issue help wanted
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interviewBubble / Data-Science-Competitions

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Goal of this repo is to provide the solutions of all Data Science Competitions(Kaggle, Data Hack, Machine Hack, Driven Data etc...).

data-science machine-learning kaggle kaggle-competition xgboost data-science-competition competition-code analytics-vidhya data-science-competitions datahack-competition machinehack-competition kaggle-winning-solutions-github kaggle-competition-solutions kaggle-competition-for-beginners kaggle-solutions-github competitive-data-science-github
  • Updated Jan 30, 2020

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