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h2oai / h2o-3

Star 5.8k

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 Mar 20, 2022
  • Jupyter Notebook
awesome-decision-tree-papers

benedekrozemberczki / awesome-decision-tree-papers

Sponsor
Star 1.9k

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
mljar-supervised

mljar / mljar-supervised

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Open

r2 metric report values

7
moshe-rl
moshe-rl commented Nov 30, 2021

When using r2 as eval metric for regression task (with 'Explain' mode) the metric values reported in Leaderboard (at README.md file) are multiplied by -1.
For instance, the metric value for some model shown in the Leaderboard is -0.41, while when clicking the model name leads to the detailed results page - and there the value of r2 is 0.41.
I've noticed that when one of R2 metric values in the L

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bug help wanted good first issue
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microsoft / FLAML

Star 1.8k
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kk7nc / Text_Classification

Star 1.5k

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 Mar 9, 2022
  • Python
awesome-fraud-detection-papers

benedekrozemberczki / awesome-fraud-detection-papers

Sponsor
Star 1.1k

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

LiYangHart / Hyperparameter-Optimization-of-Machine-Learning-Algorithms

Star 842

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 Mar 16, 2022
  • Jupyter Notebook
awesome-gradient-boosting-papers

benedekrozemberczki / awesome-gradient-boosting-papers

Sponsor
Star 782

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

LAMDA-NJU / Deep-Forest

Star 734
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

Star 303

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 Feb 16, 2022
  • Python

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