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matlab function fitcnet  (MathWorks Inc)


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    MathWorks Inc matlab function fitcnet
    Matlab Function Fitcnet, 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/product/function+fitcnet/bio_rxiv__2025__06__09__658645-392-2-5
    Average 90 stars, based on 1 article reviews
    matlab function fitcnet - by Bioz Stars, 2026-10
    90/100 stars

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    Plasmid Preparation:

    Article Title: CT-Based Radiomics to Predict KRAS Mutation in CRC Patients Using a Machine Learning Algorithm: A Retrospective Study
    Article Snippet: Neural Network , mlp: multilayer perceptron , Matlab , Function fitcnet, n h = 10, h 1 = max(1,⌊ N/(I + C) ⌋), h 0 = max(1,⌊ h 1 / n h ⌋),Δ = max(1,( h 1 - h 0 )/ n h ), h (number of hidden neurons) = h 0 :Δ: h 1 N = no. training patterns, I = no. features.



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    function fitcnet  (MathWorks Inc)
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    MathWorks Inc function fitcnet
    List of classifiers with their implementation language, function, and module/package used and values used for hyper-parameter tuning (the notation 1:2:5 means values from 1 to 5 with step 2).
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    List of classifiers with their implementation language, function, and module/package used and values used for hyper-parameter tuning (the notation 1:2:5 means values from 1 to 5 with step 2).

    Journal: Biomedicines

    Article Title: CT-Based Radiomics to Predict KRAS Mutation in CRC Patients Using a Machine Learning Algorithm: A Retrospective Study

    doi: 10.3390/biomedicines11082144

    Figure Lengend Snippet: List of classifiers with their implementation language, function, and module/package used and values used for hyper-parameter tuning (the notation 1:2:5 means values from 1 to 5 with step 2).

    Article Snippet: Neural Network , mlp: multilayer perceptron , Matlab , Function fitcnet, n h = 10, h 1 = max(1,⌊ N/(I + C) ⌋), h 0 = max(1,⌊ h 1 / n h ⌋),Δ = max(1,( h 1 - h 0 )/ n h ), h (number of hidden neurons) = h 0 :Δ: h 1 N = no. training patterns, I = no. features.

    Techniques: Plasmid Preparation