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LightGBM

microsoft / LightGBM

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[R-package] use keyword arguments for all internal function calls

jameslamb
jameslamb commented Sep 13, 2020

Summary

Today in the R package, there are a lot of internal function calls which use only positional arguments. Change them to use keyword arguments for extra safety.

I've added this issue to provide a small, focused contribution opportunity for Hacktoberfest 2020 participants. If you are an experienced open source contributor, please leave this

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catboost / catboost

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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 1, 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

arnaldog12 / Machine_Learning

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Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.

python machine-learning neural-network naive-bayes linear-regression machine-learning-algorithms regression feature-selection logistic-regression kmeans adaboost decision-trees polynomial-regression knn principal-component-analysis redes-neurais-artificiais linear-discriminant-analysis multilinear-regression
  • Updated Mar 21, 2019
  • Jupyter Notebook

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 Jul 14, 2020
  • Python

jayshah19949596 / Machine-Learning-Models

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Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means

neural-network random-forest linear-regression machine-learning-algorithms naive-bayes-classifier supervised-learning gaussian-mixture-models logistic-regression kmeans decision-trees knn principal-component-analysis dynamic-time-warping kmeans-clustering em-algorithm kmeans-algorithm singular-value-decomposition knn-classification gaussian-classifier value-iteration-algorithm
  • Updated May 15, 2017
  • MATLAB

RuleBasedIntegration / Rubi

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MBKraus / Predicting_real_estate_prices_using_scikit-learn

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Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)

real-estate python machine-learning neural-network random-forest lasso xgboost polynomial ensemble-learning ols decision-trees ridge-regression polynomial-regression knn multi-layer-perceptron amsterdam predicting-housing-prices lasso-regression mlp-regressor knn-regression
  • Updated Apr 9, 2019
  • Python

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