Overview: Interpretability tools make machine learning models more transparent by displaying how each feature influences ...

In past roles, I’ve spent countless hours trying to understand why state-of-the-art models produced subpar outputs. The underlying issue here is that machine learning models don’t “think” like humans ...

Using Real-World Data for Machine-Learning Algorithms to Predict the Treatment Response in Advanced Melanoma: A Pilot Study for Personalizing Cancer Care This study aims to investigate the impact of ...

Explainable AI plays a central role in validating model behavior. Using established explainability techniques, the study examines which financial variables drive fraud predictions. The results show a ...

This course explores the field of Explainable AI (XAI), focusing on techniques to make complex machine learning models more transparent and interpretable. Students will learn about the need for XAI, ...

Bernice Asantewaa Kyere on modeling that immediately caught my attention. The paper titled “A Critical Examination of Transformational Leadership in Implementing Flipped Classrooms for Mathematics ...

In data analysis, time series forecasting relies on various machine learning algorithms, each with its own strengths. However, we will talk about two of the most used ones. Long Short-Term Memory ...

Adnan and colleagues evaluated machine learning models’ ability to screen for Parkinson’s disease using self-recorded smile videos. 2. The models achieved high sensitivity and specificity among ...