Pinned Repositories
ACII_AffectMove_2021
benchm-ml
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
ENTROPY_2021
IJCNN2022_Balls
IJCNN2022_Ellipses
interactive-tutorials
Interactive Tutorials
NIPS2019_Fairness
Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification
NIPS2020_Fairness
ORE_2021
reforest
Random Forests in Apache Spark
lucaoneto's Repositories
lucaoneto/NIPS2020_Fairness
lucaoneto/ENTROPY_2021
lucaoneto/IJCNN2022_Ellipses
lucaoneto/reforest
Random Forests in Apache Spark
lucaoneto/ACII_AffectMove_2021
lucaoneto/benchm-ml
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
lucaoneto/IJCNN2022_Balls
lucaoneto/interactive-tutorials
Interactive Tutorials
lucaoneto/NIPS2019_Fairness
Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification
lucaoneto/ORE_2021