GitHub / randleon / Understanding-Classification-Model-Performance-Metrics-On-Diabetes-Dataset

Evaluation of the performance of classification models can be facilitated through a combination of calculating certain types of performance metrics and generating model performance evaluation graphics. The purpose of this exercise is to calculate a suite of classification model performance metrics via Python code functions.

JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/randleon%2FUnderstanding-Classification-Model-Performance-Metrics-On-Diabetes-Dataset

Topics: accuracy-metrics, areaundercurve, confusion-matrix, f1-score, precision, roccurve, sensitivity

• Files
• Readme
• Dependencies