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Cross‐validated permutation feature importance considering correlation between features
Analytical Science Advances, 2022 In molecular design, material design, process design, and process control, it is important not only to construct a model with high predictive ability between explanatory features x and objective features y using a dataset but also to interpret the ...
Hiromasa Kaneko
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Nonparametric Feature Impact and Importance
Information SciencesPractitioners use feature importance to rank and eliminate weak predictors during model development in an effort to simplify models and improve generality. Unfortunately, they also routinely conflate such feature importance measures with feature impact, the isolated effect of an explanatory variable on the response variable. This can lead to real-world
Terence Parr, James D Wilson
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Relative Feature Importance [PDF]
2020 25th International Conference on Pattern Recognition (ICPR), 2021 Interpretable Machine Learning (IML) methods are used to gain insight into the relevance of a feature of interest for the performance of a model. Commonly used IML methods differ in whether they consider features of interest in isolation, e.g., Permutation Feature Importance (PFI), or in relation to all remaining feature variables, e.g., Conditional ...
Gunnar König +3 more
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On the Importance of Encrypting Deep Features [PDF]
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021 First ...
Xingyang Ni, Heikki Huttunen, Esa Rahtu
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Partial dependence through stratification
Machine Learning with Applications, 2021 Partial dependence curves (FPD) are commonly used to explain feature importance once a supervised learning model has been fitted to data. However, it is common for the same partial dependence algorithm to give meaningfully different curves for different ...
Terence Parr, James D. Wilson
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Determinants of Tourism Demand Using Machine Learning Techniques [PDF]
African Journal of Hospitality, Tourism and Leisure, 2022 The purpose of the current study was to determine factors affecting tourism demand using machine learning techniques. The results of different linear regression and random forest models on both the train and test sets were compared using RMSE and R2 ...
Musonera Abdou +2 more
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Time series classification with random temporal features
Journal of King Saud University: Computer and Information Sciences, 2023 Time series classification exists in widespread domains such as EEG/ECG classification, device anomaly detection, and speaker authentication. Although many methods have been proposed, efficient selection of intuitive temporal features to accurately ...
Cun Ji +6 more
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BMC Medical Research Methodology, 2023 Background Machine learning tools such as random forests provide important opportunities for modeling large, complex modern data generated in medicine.
Meredith L. Wallace +8 more
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Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection
Entropy, 2022 Gradient Boosting Machines (GBM) are among the go-to algorithms on tabular data, which produce state-of-the-art results in many prediction tasks. Despite its popularity, the GBM framework suffers from a fundamental flaw in its base learners. Specifically,
Afek Ilay Adler, Amichai Painsky
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Applied Sciences, 2021 Diabetes Mellitus (DM) is one of the most common chronic diseases leading to severe health complications that may cause death. The disease influences individuals, community, and the government due to the continuous monitoring, lifelong commitment, and ...
Hafiz Farooq Ahmad +4 more
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