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h2oai / h2o-3

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Open Source Fast Scalable Machine Learning Platform For Smarter Applications: Deep Learning, Gradient Boosting & XGBoost, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

python java data-science machine-learning multi-threading opensource r big-data spark deep-learning hadoop random-forest gpu naive-bayes h2o distributed pca gbm ensemble-learning automl
  • Updated Oct 6, 2020
  • Jupyter Notebook
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

kk7nc / Text_Classification

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Text Classification Algorithms: A Survey

deep-learning random-forest text-classification recurrent-neural-networks naive-bayes-classifier dimensionality-reduction logistic-regression document-classification convolutional-neural-networks text-processing decision-trees boosting-algorithms support-vector-machines hierarchical-attention-networks nlp-machine-learning conditional-random-fields k-nearest-neighbours deep-belief-network rocchio-algorithm deep-neural-network
  • Updated Aug 25, 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 Aug 2, 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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LiYangHart / Hyperparameter-Optimization-of-Machine-Learning-Algorithms

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Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

machine-learning deep-learning random-forest optimization svm genetic-algorithm machine-learning-algorithms hyperparameter-optimization artificial-neural-networks grid-search tuning-parameters knn bayesian-optimization hyperparameter-tuning random-search particle-swarm-optimization hpo python-examples python-samples hyperband
  • Updated Aug 30, 2020
  • Jupyter Notebook

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