Designing Explainable Predictive Machine Learning Artifacts

Designing Explainable Predictive Machine Learning Artifacts To contribute to overcome this adoption barrier, we argue that research in information systems should devote more attention to the design of prototypical prediction oriented machine learning applications (i.e., artifacts) whose predictions can be explained to human decision makers. To contribute to overcome this adoption barrier, we argue that research in information systems should devote more attention to the design of prototypical prediction oriented machine learning.

Designing Explainable Predictive Machine Learning Artifacts In practice, if a corresponding ml model is attributed to possess a sufficient degree of predictive power, it may be deployed in a productive environment to compute real world predictions, e.g., to support managerial decision making. This paper is focusing on developing visually interpretable machine learning models for flight science engineers, which would help them in validating the performance and reliability of developed models created on flight science concepts. For this reason, we develop a methodology which unifies methodological knowledge from design science research and predictive analytics with state of the art approaches to explainable artificial intelligence. Bibliographic details on designing explainable predictive machine learning artifacts: methodology and practical demonstration.

Designing Explainable Predictive Machine Learning Artifacts For this reason, we develop a methodology which unifies methodological knowledge from design science research and predictive analytics with state of the art approaches to explainable artificial intelligence. Bibliographic details on designing explainable predictive machine learning artifacts: methodology and practical demonstration. Designing explainable predictive machine learning artifacts: methodology and practical demonstration. This study proposes an explainable machine learning approach by integrating the random forest algorithm and the shap (shapley additive explanations) method to predict and interpret employee attrition behavior more transparently. This book is for scientists, researchers, engineers, architects, and managers who are actively engaged in machine learning and related fields. anyone who is interested in problem solving using ai will benefit from this book. The study aims to develop an advanced explainable hybrid predictive model by combining two top performing algorithms, extra tree (ensemble learning) and multilayer perceptron (deep learning), to.