Towards Explainability of Tree-Based Ensemble Models. A Critical Overview

Sepioło, Dominik; Ligęza, Antoni

doi:10.1007/978-3-031-06746-4_28

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…These models can be employed for both regression tasks, which predict numerical outcomes, and classification tasks, which predict categorical outcomes. A major advantage of treebased models is that they are easy to interpret as they create a set of if-else rules in a human-understandable way to predict an outcome (30,31). A drawback is that these if-else rules can become very specific to the dataset on which they are fitted, potentially causing overfitting (32).…”

Section: Tree-based Modelsmentioning

confidence: 99%

Advancing spine care through AI and machine learning: overview and applications

Cina,

Galbusera

2024

EFORT Open Reviews

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Machine learning (ML), a subset of artificial intelligence, is crucial for spine care and research due to its ability to improve treatment selection and outcomes, leveraging the vast amounts of data generated in health care for more accurate diagnoses and decision support. ML's potential in spine care is particularly notable in radiological image analysis, including the localization and labeling of anatomical structures, detection and classification of radiological findings, and prediction of clinical outcomes, thereby paving the way for personalized medicine. The manuscript discusses ML's application in spine care, detailing supervised and unsupervised learning, regression, classification, and clustering, and highlights the importance of both internal and external validation in assessing ML model performance. Several ML algorithms such as linear models, support vector machines, decision trees, neural networks, and deep convolutional neural networks, can be used in the spine domain to analyze diverse data types (visual, tabular, omics, and multimodal).

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Section: Tree-based Modelsmentioning

confidence: 99%