Explainable Machine Learning
Explainable Machine Learning - Related Videos
06:19
Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Cited by 4 •
2025
Mime is a flexible computational framework to construct a machine learning-based integration model with elegant performance. Here, we provide a detailed step-by-step procedure for developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with disease progression, patient outcomes, and therapeutic response.
04:04
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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2025
This study employed voice signal analysis and machine learning methods, utilizing MATLAB to extract distinctive voice features for non-invasive early detection of asthma. The Support Vector Machine (SVM) and Random Forest (RF) algorithms demonstrated comparable performance in terms of overall classification accuracy, although SVM may achieve a better balance between sensitivity and specificity.
11:38
A Live-cell Image-Based Machine Learning Strategy to Monitor Pluripotent Stem Cell Differentiation
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2024
Available pluripotent stem cell (PSC)-to-functional cell differentiation systems are currently impeded by problems of severe line-to-line and batch-to-batch variability. Here, using cardiac differentiation as the main example, we present a protocol to intelligently monitor and modulate the process of PSC differentiation based on image-based machine learning.
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.
10:40
Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography
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Cited by 5 •
2025
We present a step-by-step protocol for high-resolution, label-free, and three-dimensional imaging of organoids using low-coherence holotomography. This protocol details organoid culture preparation, imaging acquisition, and computational image analysis, enabling real-time visualization of structural dynamics and drug responses in living organoids.
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.