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h2oai / h2o-3

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H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), 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 Sep 3, 2021
  • Jupyter Notebook
orange3

biolab / orange3

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Open

Rank Widget: Variables as column, not header

janezd
janezd commented Aug 28, 2021

What's your use case?
In other words, what's your pain point?

Variable names and their icons are shown as vertical header. This

  • is ugly,
  • doesn't show the selection properly,
  • doesn't allow sorting by variable names,
  • doesn't allow selection by dragging across a range of variables (though one can drag across rows in the table itself),
  • and possibly something else.

<img wi

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good first issue
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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 31, 2021
  • 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 Apr 9, 2021
  • Python
awesome-fraud-detection-papers

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 Aug 29, 2021
  • 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 29, 2021
  • Python

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 19, 2021
  • Jupyter Notebook

LAMDA-NJU / Deep-Forest

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Open

Better Documentation

4
xuyxu
xuyxu commented Feb 12, 2021

Thanks to the contributors, many new features have been developed. As a result, the current version of documentation could be ambiguous, and requires more explanation or demonstration.

This issue collects suggestions on the documentation. Any one is welcomed to improve the readability of the documentation. For contributors unfamiliar with our workflow on building the documentation, please refe

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good first issue

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
python data-science machine-learning data-mining random-forest kaggle id3 gbdt gbm gbrt gradient-boosting-machine cart adaboost decision-trees gradient-boosting c45-trees categorical-features gradient-boosting-machines regression-tree
  • Updated Jun 29, 2021
  • Python

Skylark0924 / Machine-Learning-is-ALL-You-Need

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🔥🌟《Machine Learning 格物志》: ML + DL + RL basic codes and notes by sklearn, PyTorch, TensorFlow, Keras & the most important, from scratch!💪 This repository is ALL You Need!
qlearning random-forest tensorflow keras deep-reinforcement-learning pytorch lstm gan dqn naive-bayes-classifier logistic-regression resnet convolutional-neural-networks support-vector-machine decision-trees ddpg k-nearest-neighbours actor-critic trpo ppo
  • Updated Dec 14, 2020
  • Python

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