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matlab's 'fitcensemble' function  (MathWorks Inc)


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    MathWorks Inc matlab's 'fitcensemble' function
    Comparing non-hierarchical and hierarchical approaches for classifying calls based on source identity. Mean ± s.d. precisions and recalls with corresponding p -values for testing the hypotheses: mean precisions/recalls of <t> non-hierarchical = hierarchical. </t> Both approaches were tested using the Balanced-X datasets.
    Matlab's 'fitcensemble' Function, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/matlab's 'fitcensemble' function/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    matlab's 'fitcensemble' function - by Bioz Stars, 2026-04
    90/100 stars

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    1) Product Images from "Who is calling? Optimizing source identification from marmoset vocalizations with hierarchical machine learning classifiers"

    Article Title: Who is calling? Optimizing source identification from marmoset vocalizations with hierarchical machine learning classifiers

    Journal: Journal of the Royal Society Interface

    doi: 10.1098/rsif.2023.0399

    Comparing non-hierarchical and hierarchical approaches for classifying calls based on source identity. Mean ± s.d. precisions and recalls with corresponding p -values for testing the hypotheses: mean precisions/recalls of non-hierarchical = hierarchical. Both approaches were tested using the Balanced-X datasets.
    Figure Legend Snippet: Comparing non-hierarchical and hierarchical approaches for classifying calls based on source identity. Mean ± s.d. precisions and recalls with corresponding p -values for testing the hypotheses: mean precisions/recalls of non-hierarchical = hierarchical. Both approaches were tested using the Balanced-X datasets.

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    Comparing non-hierarchical and hierarchical approaches for classifying calls based on source identity. Mean ± s.d. precisions and recalls with corresponding p -values for testing the hypotheses: mean precisions/recalls of <t> non-hierarchical = hierarchical. </t> Both approaches were tested using the Balanced-X datasets.
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    fitcensemble matlab function  (MathWorks Inc)
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    Comparing non-hierarchical and hierarchical approaches for classifying calls based on source identity. Mean ± s.d. precisions and recalls with corresponding p -values for testing the hypotheses: mean precisions/recalls of <t> non-hierarchical = hierarchical. </t> Both approaches were tested using the Balanced-X datasets.
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    Comparing non-hierarchical and hierarchical approaches for classifying calls based on source identity. Mean ± s.d. precisions and recalls with corresponding p -values for testing the hypotheses: mean precisions/recalls of non-hierarchical = hierarchical. Both approaches were tested using the Balanced-X datasets.

    Journal: Journal of the Royal Society Interface

    Article Title: Who is calling? Optimizing source identification from marmoset vocalizations with hierarchical machine learning classifiers

    doi: 10.1098/rsif.2023.0399

    Figure Lengend Snippet: Comparing non-hierarchical and hierarchical approaches for classifying calls based on source identity. Mean ± s.d. precisions and recalls with corresponding p -values for testing the hypotheses: mean precisions/recalls of non-hierarchical = hierarchical. Both approaches were tested using the Balanced-X datasets.

    Article Snippet: We first trained AdaBoost (using MATLAB's ‘fitcensemble’, ‘AdaboostM1′, and ‘AdaboostM2′ functions) on Imbalanced-X, Balanced-X, Balanced197-X, and Balanced99-X datasets to classify calls based on source identity—as a direct or ‘non-hierarchical approach’ (in contrast to the hierarchical approach that was used later)—to determine source identity from calls.

    Techniques: