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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 Jun 5, 2022
  • Jupyter Notebook
awesome-decision-tree-papers

benedekrozemberczki / awesome-decision-tree-papers

Sponsor
Star 2k

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

Star 1.9k
Open

Path normalization (train on Windows and deploy on Linux)

2
ViacheslavDanilov
ViacheslavDanilov commented May 19, 2022

I trained models on Windows, then I tried to use them on Linux, however, I could not load them due to an incorrect path joining. During model loading, I got learner_path in the following format experiments_dir/model_1/100_LightGBM\\learner_fold_0.lightgbm. The last two slashes were incorrectly concatenated with the rest part of the path. In this regard, I would suggest adding something like `l

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bug help wanted good first issue
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kk7nc / Text_Classification

Star 1.6k

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 926

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 Apr 4, 2022
  • Jupyter Notebook
awesome-gradient-boosting-papers

benedekrozemberczki / awesome-gradient-boosting-papers

Sponsor
Star 792

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 762
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

grf-labs / grf

Star 691
Open

Provide optional printout of progress

3
tibshirani
tibshirani commented Sep 5, 2018

I ran a regression_forest for > 10 minutes and had no idea if it would complete in 15 min or an hour.

It would be great to have an argument "verbose" (default FALSE) which causes the function to
print the function's progress, to help the user estimate the remaining time before completion.

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feature good first issue

serengil / chefboost

Star 332

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 May 21, 2022
  • Python

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