Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.
created at Sept. 4, 2015, 4:33 p.m.
A collection of pandas & scikit-learn compatible transformers for preprocessing and feature engineering ðŸ›
created at Sept. 18, 2022, 1:52 p.m.
Scikit-learn compatible wrapper of the Random Bits Forest program written by (Wang et al., 2016)
created at July 31, 2016, 3:17 p.m.
TensorLight - A high-level framework for TensorFlow
created at Oct. 19, 2016, 2:35 p.m.
Universal 1d/2d data containers with Transformers functionality for data analysis.
created at Aug. 22, 2017, 3:52 p.m.
Auralisation of learned features in CNN (for audio)
created at Dec. 4, 2015, 3 p.m.
scikit-learn addon to operate on set/"group"-based features
created at June 10, 2014, 10:36 p.m.
Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University
created at July 9, 2018, 1:44 p.m.
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.
created at April 22, 2017, 10:16 p.m.
SigOpt wrappers for scikit-learn methods
created at April 15, 2016, 8:58 p.m.
A graph reliability toolbox based on PyTorch and PyTorch Geometric (PyG).
created at Nov. 15, 2021, 1:06 a.m.
Pandas-based utility to calculate weighted means, medians, distributions, standard deviations, and more.
created at Dec. 23, 2016, 5:09 a.m.