API reference
AstronomicAL is a human-in-the-loop interactive labelling and training dashboard that allows users to create reliable datasets and robust classifiers using active learning. The system enables users to visualise and integrate data from different sources and deal with incorrect or missing labels and imbalanced class sizes by using active learning to help the user focus on correcting the labels of a few key examples. Combining the use of the Panel , Bokeh , modAL and SciKit Learn packages, AstronomicAL enables
With ever-growing datasets, it is becoming impossible to manually inspect and verify ground truth used to train machine learning systems. The reliability of the training data limits the performance of any supervised learning model, so consistent classifications become more problematic as data sizes increase. The problem is exacerbated when a dataset does not contain any labelled data, preventing supervised learning techniques entirely. Active learning (Settles, 2012) addresses these issues by removing the r