Evaluation of supervised predictions for two-class and multi-class classifiers
[Master Thesis 2017] Scripts for calculating metrics to assess performance of a drug design software.
Python code to obtain metrics like receiver operating characteristics (ROC) curve and area under the curve (AUC) from scratch without using in-built functions.
The repository contains the code for the various machine learning algorithms used to make a predictive analysis of tweets on GST in India
This repository trains and evaluates three CNN models on MNIST, providing performance comparisons and 5 unique visualizations.
Light-weight package for classification metrics computed on streams or minibatches of data. Mainly for area under the curve (AUC) of precision-recall (PR) or receiver operating characteristic (ROC) curves. Supports multi-class setting with either macro- or micro aggregation..
Add a description, image, and links to the receiver-operating-characteristic topic page so that developers can more easily learn about it.
To associate your repository with the receiver-operating-characteristic topic, visit your repo's landing page and select "manage topics."