ModelWise: Interactive Model Comparison for Model Diagnosis, Improvement and Selection

Meng, Linhao; Elzen, Stef van den; Vilanova, Anna

doi:10.1111/cgf.14525

Cited by 9 publications

(2 citation statements)

References 44 publications

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“…In [31], the authors developed a visual method to compare multiple classifiers considering model performance, feature space, and model explanation. ModelWise adapts visualizations with rich interactions to support multiple workflows to achieve a model diagnosis, improvement, and selection.…”

Section: Interactive Machine Learningmentioning

confidence: 99%

Ask Your Data—Supporting Data Science Processes by Combining AutoML and Conversational Interfaces

et al. 2023

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Data Science is increasingly applied for solving real-life problems, in industry and in academic research, but mastering Data Science requires an interdisciplinary education that is still scarce on the market. Thus, there is a growing need for user-friendly tools that allow domain experts to directly apply data analysis methods to their datasets, without involving a Data Science expert. In this scenario, we present DSBot, an assistant that can analyze the user data and produce answers by mastering several Data Science techniques. DSBot understands the research question with the help of conversation interaction, produces a data science pipeline and automatically executes the pipeline in order to generate analysis. The strength of DSBot lies in the design of a rich domain specific language for modeling data analysis pipelines, the use of a suitable neural network for machine translation of research questions, the availability of a vast dictionary of pipelines for matching the translation output, and the use of natural language technology provided by a conversational agent. We empirically evaluated the translation capabilities and the autoML performances of the system. In the translation task, it obtains a BLEU score of 0.8. In prediction tasks, DSBot outperforms TPOT, an autoML tool, in 19 datasets out of 30.

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Section: Interactive Machine Learningmentioning

confidence: 99%

Ask Your Data—Supporting Data Science Processes by Combining AutoML and Conversational Interfaces

et al. 2023

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“…Users can also check how many misclassified instances exist in each model and propagate from one model to another for each label class. Inspired by previous works [409,454,675], we utilize a distinct visual metaphor for this plot to convey-as concisely as possible-the per-class confusion for the several under examination ML models. The bar charts in Figure 8.…”

Section: Models Overviewmentioning

confidence: 99%

Visual Analytics for Explainable and Trustworthy Machine Learning

Chatzimparmpas¹

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The deployment of artificial intelligence solutions and machine learning research has exploded in popularity in recent years, with numerous types of models proposed to interpret and predict patterns and trends in data from diverse disciplines. However, as the complexity of these models grows, it becomes increasingly difficult for users to evaluate and rely on the model results, since their inner workings are mostly hidden in black boxes, which are difficult to trust in critical decision-making scenarios. While automated methods can partly handle these problems, recent research findings suggest that their combination with innovative methods developed within information visualization and visual analytics can lead to further insights gained from models and, consequently, improve their predictive ability and enhance trustworthiness in the entire process. Visual analytics is the area of research that studies the analysis of vast and intricate information spaces by combining statistical and machine learning models with interactive visual interfaces. By following this methodology, human experts can better understand such spaces and apply their domain expertise in the process of building and improving the underlying models. The primary goals of this dissertation are twofold, focusing on (1) methodological aspects, by conducting qualitative and quantitative meta-analyses to support the visualization research community in making sense of its literature and to highlight unsolved challenges, as well as (2) technical solutions, by developing visual analytics approaches for various machine learning models, such as dimensionality reduction and ensemble learning methods. Regarding the first goal, we define, categorize, and examine in depth the means for visual coverage of the different trust levels at each stage of a typical machine learning pipeline and establish a design space for novel visualizations in the area. Regarding the second goal, we discuss multiple visual analytics tools and systems implemented by us to facilitate the underlying research on the various stages of the machine learning pipeline, i.e., data processing, feature engineering, hyperparameter tuning, understanding, debugging, refining, and comparing models. Our approaches are data-agnostic, but mainly target tabular data with meaningful attributes in diverse domains, such as health care and finance. The applicability and effectiveness of this work were validated with case studies, usage scenarios, expert interviews, user studies, and critical discussions of limitations and alternative designs. The results of this dissertation provide new avenues for visual analytics research in explainable and trustworthy machine learning. Keywords: visualization, interaction, visual analytics, explainable machine learning, XAI, trustworthy machine learning, ensemble learning, dimensionality reduction, supervised learning, unsupervised learning, ML, AI, tabular data

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Democratizing Data Science:Using Language Models for Intuitive Data Insights and Visualizations

Sv,

et al. 2024

2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN)

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ModelWise: Interactive Model Comparison for Model Diagnosis, Improvement and Selection

Cited by 9 publications

References 44 publications

Ask Your Data—Supporting Data Science Processes by Combining AutoML and Conversational Interfaces

Ask Your Data—Supporting Data Science Processes by Combining AutoML and Conversational Interfaces

Visual Analytics for Explainable and Trustworthy Machine Learning

Democratizing Data Science:Using Language Models for Intuitive Data Insights and Visualizations

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