Interpretable machine learning–a brief history, state-of-the-art and challenges
We present a brief history of the field of interpretable machine learning (IML), give an
overview of state-of-the-art interpretation methods and discuss challenges. Research in IML …
overview of state-of-the-art interpretation methods and discuss challenges. Research in IML …
General pitfalls of model-agnostic interpretation methods for machine learning models
An increasing number of model-agnostic interpretation techniques for machine learning
(ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) …
(ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) …
Grouped feature importance and combined features effect plot
Interpretable machine learning has become a very active area of research due to the rising
popularity of machine learning algorithms and their inherently challenging interpretability …
popularity of machine learning algorithms and their inherently challenging interpretability …
Alpha oscillations link action to cognition: An oculomotor account of the brain's dominant rhythm
Power modulations in alpha oscillations (8-14Hz) have been associated with most human
cognitive functions and psychopathological conditions studied. These reports are often …
cognitive functions and psychopathological conditions studied. These reports are often …
Variable importance in high-dimensional settings requires grouping
Explaining the decision process of machine learning algorithms is nowadays crucial for both
model's performance enhancement and human comprehension. This can be achieved by …
model's performance enhancement and human comprehension. This can be achieved by …
Spatiotemporally resolved multivariate pattern analysis for M/EEG
An emerging goal in neuroscience is tracking what information is represented in brain
activity over time as a participant completes some task. While electroencephalography …
activity over time as a participant completes some task. While electroencephalography …
[HTML][HTML] Explaining the predictions of kernel SVM models for neuroimaging data analysis
Abstract Machine learning methods have shown great performance in many areas, including
neuroimaging data analysis. However, model performance is only one objective in …
neuroimaging data analysis. However, model performance is only one objective in …
[HTML][HTML] The relationship between frequency content and representational dynamics in the decoding of neurophysiological data
Decoding of high temporal resolution, stimulus-evoked neurophysiological data is
increasingly used to test theories about how the brain processes information. However, a …
increasingly used to test theories about how the brain processes information. However, a …
Machine Learning Insights: Exploring Key Factors Influencing Sale-to-List Ratio—Insights from SVM Classification and Recursive Feature Selection in the US Real …
J Sobieraj, D Metelski - Buildings, 2024 - mdpi.com
The US real estate market is a complex ecosystem influenced by multiple factors, making it
critical for stakeholders to understand its dynamics. This study uses Zillow Econ (monthly) …
critical for stakeholders to understand its dynamics. This study uses Zillow Econ (monthly) …
[PDF][PDF] An Explainable AI Handbook for Psychologists: Methods, Opportunities, and Challenges
R Lavelle-Hill, G Smith, K Murayama - 2024 - files.osf.io
With more researchers in psychology using machine learning to model large datasets, many
are also looking to eXplainable AI (XAI) to understand how their model works and to gain …
are also looking to eXplainable AI (XAI) to understand how their model works and to gain …