Interactive analysis of high-dimensional association structures with graphical models

Graphical chain models are a capable tool for analyzing multivariate data. However, their practical use may still be cumbersome in some respect since fitting the model requires the application of an intensive selection strategy based on the calculation of an enormous number of different regressions. In this paper, we present a computer system especially designed for the calculation of graphical chain models which is not only planned to automatically carry out the model search but also to visualize the corresponding graph at each stage of the model fit on request by the user. It additionally allows to modify the graph and the model fit interactively

Predicting regression test failures using genetic algorithm-selected dynamic performance analysis metrics

A novel framework for predicting regression test failures is proposed. The basic principle embodied in the framework is to use performance analysis tools to capture the runtime behaviour of a program as it executes each test in a regression suite. The performance information is then used to build a dynamically predictive model of test outcomes. Our framework is evaluated using a genetic algorithm for dynamic metric selection in combination with state-of-the-art machine learning classifiers. We show that if a program is modified and some tests subsequently fail, then it is possible to predict with considerable accuracy which of the remaining tests will also fail which can be used to help prioritise tests in time constrained testing environments

API design for machine learning software: experiences from the scikit-learn project

Scikit-learn is an increasingly popular machine learning li- brary. Written in Python, it is designed to be simple and efficient, accessible to non-experts, and reusable in various contexts. In this paper, we present and discuss our design choices for the application programming interface (API) of the project. In particular, we describe the simple and elegant interface shared by all learning and processing units in the library and then discuss its advantages in terms of composition and reusability. The paper also comments on implementation details specific to the Python ecosystem and analyzes obstacles faced by users and developers of the library