In Machine Learning, a supervised model's performance is measured using the evaluation metrics. In this study, we first present our motivation by revisiting the major limitations of these metrics, namely one-dimensionality, lack of context, lack of intuitiveness, uncomparability, binary restriction, and uncustomizability of metrics. In response, we propose Contingency Space, a bounded semimetric space that provides a generic representation for any performance evaluation metric. Then we showcase how this space addresses the limitations. In this space, each metric forms a surface using which we visually compare different evaluation metrics. Taking advantage of the fact that a metric's surface warps proportionally to the degree of which it is sensitive to the class-imbalance ratio of data, we introduce Imbalance Sensitivity that quantifies the skew-sensitivity. Since an arbitrary model is represented in this space by a single point, we introduce Learning Path for qualitative and quantitative analyses of the training process. Using the semimetric that contingency space is endowed with, we introduce Tau as a new cost sensitive and Imbalance Agnostic metric. Lastly, we show that contingency space addresses multi-class problems as well. Throughout this work, we define each concept through stipulated definitions and present every application with practical examples and visualizations.

Solar energetic particles are a result of intense solar events such as solar flares and Coronal Mass Ejections (CMEs). These latter events all together can cause major disruptions to spacecraft that are in Earth's orbit and outside of the magnetosphere. In this work we are interested in establishing the necessary conditions for a major geo-effective solar particle storm immediately after a major flare, namely the existence of a direct magnetic connection. To our knowledge, this is the first work that explores not only the correlations of GOES X-ray and proton channels, but also the correlations that happen across all the proton channels. We found that proton channels autocorrelations and cross-correlations may also be precursors to the occurrence of an SEP event. In this paper, we tackle the problem of predicting >100 MeV SEP events from a multivariate time series perspective using easily interpretable decision tree models.

This paper introduces a new public benchmark dataset of solar image data from the Solar Dynamics Observatory (SDO) mission. This is the first release, which contains over 15,000 images and nearly 24,000 solar events, spanning the first six months of 2012. It combines region-based event labels from six automated detection modules, ten pre-computed image parameters for each cell over a grid-based segmentation of the full resolution images, and a lower resolution version of the images for further analysis and visualization. Together, these components serve as a standardized, ready-to-use, solar image dataset for general image processing research, without requiring the necessary background knowledge to properly prepare it. We present here the fundamental dataset creation details and outline future improvements and opportunities as data collection continues for the coming years.

In this work we report on the transfer of image parameters that produce good results for medical images to the domain of solar image analysis. Using the first solar domain-specific benchmark dataset that contains multiple types of solar phenomena we discovered during our work for constructing a content-based image retrieval (CBIR) system for NASA's Solar Dynamics Observatory (SDO) mission that we could take advantage of the research on the analysis of images in the medical field. We demonstrate that, while image analysis is a very domain-specific task, there are lessons to be learned and methods to be shared between different fields. In this paper we present an extensive comparative analysis of several different domain-specific datasets in order to provide some guidance for the solar physics community on the well- researched field of medical image analysis allowing them to transfer knowledge from one applied field to their own.