CACTUS: Detecting and Resolving Conflicts in Objective Functions

Abstract--Machine learning (ML) models are constructed by expert ML practitioners using various coding languages, in which they tune and select models hyperparameters and learning algorithms for a given problem domain. They also carefully design an objective function or loss function (often with multiple objectives) that captures the desired output for a given ML task such as classification, regression, etc. In multi-objective optimization, conflicting objectives and constraints is a major area of concern. In such problems, several competing objectives are seen for which no single optimal solution is found that satisfies all desired objectives simultaneously. In the past VA systems have allowed users to interactively construct objective functions for a classifier. In this paper, we extend this line of work by prototyping a technique to visualize multi-objective objective functions either defined in a Jupyter notebook or defined using an interactive visual interface to help users to: (1) perceive and interpret complex mathematical terms in it and (2) detect and resolve conflicting objectives. Visualization of the objective function enlightens potentially conflicting objectives that obstructs selecting correct solution(s) for the desired ML task or goal. We also present an enumeration of potential conflicts in objective specification in multi-objective objective functions for classifier selection. Furthermore, we demonstrate our approach in a VA system that helps users in specifying meaningful objective functions to a classifier by detecting and resolving conflicting objectives and constraints. Through a within-subject quantitative and qualitative user study, we present results showing that our technique helps users interactively specify meaningful objective functions by resolving potential conflicts for a classification task. In the past, researchers in visual analytics (VA) have investigated making ML model construction interactive, which means developing visual interfaces that allow users to construct ML models by interacting with graphical widgets or data marks [1], [2]. For example, the system XClusim helps biologists to interactively cluster a specified dataset [3], Hypermoval [4] and BEAMES [5] allows interactive construction of regression models, Axissketcher allows dimension reduction using simple drag-drop interactions [6]. Workflow adopted in the system CACTUS. Recently, Das et al. have demonstrated that may result into incorrectly predicting many relevant data a VA system, QUESTO [7] that facilitated interactive creation of instances, though improving the generalizability of the model. Here objective functions to solve a classification task utilising an Auto-the objective to train a model with high accuracy on a set of ML system.