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Evaluation metrics and statistical tests for machine learning

1,043
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March 13, 2024
Published Date

Research Abstract & Technology Focus

AbstractResearch on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with statistics, it might be difficult to understand how to evaluate the performance of ML models and compare them with each other. Here, we introduce the most common evaluation metrics used for the typical supervised ML tasks including binary, multi-class, and multi-label classification, regression, image segmentation, object detection, and information retrieval. We explain how to choose a suitable statistical test for comparing models, how to obtain enough values of the metric for testing, and how to perform the test and interpret its results. We also present a few practical examples about comparing convolutional neural networks used to classify X-rays with different lung infections and detect cancer tumors in positron emission tomography images.
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This literature focuses on: AbstractResearch on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with statistics, it might be difficult to understand how to evaluate the performance of ML mo...

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Yes, highly correlated activity was mapped. An entry titled 'Evaluation metrics and statistical tests for machine learning' discusses this: AbstractResearch on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not fa...

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Yes, highly correlated activity was mapped. An entry titled 'Ml-research' discusses this: ML research is demonstrating significant utility in scientific discovery, such as identifying exoplanets. Concurrently, the economic viability of l...

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    MindReader v1
    Read minds (simulated fMRI data, channeled to neuro-metrics)
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    PulseKit
    Your key metrics, as widgets across your Apple devices

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