Explainable Machine Learning
Explainable Machine Learning - Related Videos
06:22
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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2025
This study effectively accomplished the automated classification of two distinct categories by acquiring cough sound data from patients diagnosed with chronic obstructive pulmonary disease (COPD) and respiratory tract infections (RTI), utilizing an integration of speech signal processing techniques and machine learning algorithms.
07:13
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Cited by 1 •
2025
This study evaluates prognostic systems for colorectal signet-ring cell carcinoma patients using machine learning models and competing risk analyses. It identifies log odds of positive lymph nodes as a superior predictor compared to pN staging, demonstrating strong predictive performance and aiding clinical decision-making through robust survival prediction tools.
09:02
AC Synchronous Machine Synchronization
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2023
Source: Ali Bazzi, Department of Electrical Engineering, University of Connecticut, Storrs, CT. Three-phase wound-rotor synchronous generators are the main source of electrical power worldwide. They require a prime mover and an exciter in order to generate power. The prime mover can be a turbine spun by fluid (gas or liquid), thus the sources of the fluid can be water running off a dam through a long nozzle, steam from water evaporated using burned coal, etc. Most power plants including coal,...
06:02
Visual Statistical Learning
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University The visual environment contains massive amounts of information involving the relations between objects in space and time; certain objects are more likely to appear in the vicinity of other objects. Learning these regularities can support a wide array of visual processing, including object recognition. Unsurprisingly, then, humans appear to learn these regularities automatically, quickly, and without conscious awareness. The name...
09:16
Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
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Cited by 5 •
2020
Extracellular DNA (ecDNA) released during cell death is proinflammatory and contributes to inflammation. Measurement of ecDNA at the site of injury can determine the efficacy of therapeutic treatment in the target organ. This protocol describes the use of a machine learning tool to automate measurement of ecDNA in kidney tissue.
FAQs
Feature attribution estimates how much each input variable contributes to a particular prediction. This connects measurable conditions with the model’s output, allowing engineers to identify which variables influenced a diagnosis, maintenance prediction, or control decision. Reviewing these contributions can reveal whether the model relies on relevant conditions and can help expose errors or potential bias.
A surrogate model provides an approximation of a complex predictor in a form that is easier for people to examine. Its purpose is not to replace the original system, but to represent how inputs relate to outputs in an understandable way. Engineers can use this approximation to support validation and communicate model behavior with domain experts.
Counterfactual explanations show how changing an input could alter a result. Instead of only identifying influential variables, they describe a possible difference between the observed case and another outcome. In engineering analysis, this perspective can help users examine which conditions are associated with a changed prediction and judge whether the model’s response is meaningful.
Explanations provide evidence for examining whether a model responds to appropriate conditions, produces errors, or reflects potential bias. Feature contributions, approximating models, and counterfactual changes offer different views of the relationship between inputs and outputs. Together, these insights strengthen validation and accountability rather than requiring users to accept predictions without examining their basis.
In engineering, XAI can be applied to monitoring, fault diagnosis, predictive maintenance, and safety-critical control. The workflow uses explanations to reveal influential operating conditions and inspect model behavior alongside engineering knowledge. This supports informed review of data-driven results, helps experts investigate possible faults or errors, and improves collaboration between technical personnel and machine-learning systems.
Applying XAI can produce more than a prediction by indicating influential conditions, possible changes associated with a different result, or an understandable approximation of complex behavior. These outputs help engineers assess reliability, detect errors, and support decisions. They also contribute to validation and accountability when models are used in monitoring, maintenance, diagnosis, or control.