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fitcnb function  (MathWorks Inc)


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    MathWorks Inc fitcnb function
    Fitcnb 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/product/fitcnb+function/pm38772375-301-10-10
    Average 90 stars, based on 1 article reviews
    fitcnb function - by Bioz Stars, 2026-11
    90/100 stars

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    Article Title: Long-term stability of cortical population dynamics underlying consistent behavior
    Article Snippet: The naive Bayes classifiers, trained using Matlab (fitcnb), provided a probabilistic assignment of the inputs to one of eight classes, corresponding to the eight possible target directions.

    Article Title: Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls
    Article Snippet: For Naïve Bayes, we used the fitcnb Matlab function.


    Article Title: MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study
    Article Snippet: Last, for the Bayesian model classifier, we used the MATLAB function fitcnb , and no optimisation steps were performed.

    Article Title: Neural circuit dynamics of drug-context associative learning in the mouse hippocampus
    Article Snippet: To estimate P(X i | Y = y), we applied the built-in function of MATLAB fitcnb() to fit a multinomial distribution using the bag-of-tokens model with Laplace smoothing, which gave an estimation of distribution parameter for each neuron X i at the given spatial bin y as: 6 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{\theta }}}}}}\left({{{{{{\rm{X}}}}}}}_{{{{{{\rm{i}}}}}}}|{{{{{\rm{Y}}}}}}={{{{{\rm{y}}}}}}\right)=\frac{\mathop{\sum }\nolimits_{{{{{{\rm{j}}}}}}=1}^{{{{{{\rm{M}}}}}}}\big({{{{{{\rm{X}}}}}}}_{{{{{{\rm{i}}}}}}}=\,{{{{{{\rm{X}}}}}}}_{{{{{{\rm{i}}}}}}}|{{{{{{\rm{Y}}}}}}}^{({{{{{\rm{j}}}}}})}={{{{{\rm{y}}}}}}\big)+1}{\mathop{\sum }\nolimits_{{{{{{\rm{k}}}}}}=1}^{{{{{{\rm{N}}}}}}}\mathop{\sum }_{{{{{{\rm{j}}}}}}=1}^{{{{{{\rm{M}}}}}}}\big({{{{{{\rm{X}}}}}}}_{{{{{{\rm{i}}}}}}}={{{{{{\rm{X}}}}}}}_{{{{{{\rm{k}}}}}}}|{{{{{{\rm{Y}}}}}}}^{({{{{{\rm{j}}}}}})}={{{{{\rm{y}}}}}}\big)+{{{{{\rm{N}}}}}}},$$\end{document} θ X i ∣ Y = y = ∑ j = 1 M X i = X i ∣ Y ( j ) = y + 1 ∑ k = 1 N ∑ j = 1 M X i = X k ∣ Y ( j ) = y + N , where, regardless of the Laplace smoothing, the numerator is the total number of spikes of neuron X i for the time bins that the animal is at location y, and the denominator is the total number of spikes of all the neurons for the time bins that the animals is at location y.

    Article Title: Threat-dependent scaling of prelimbic dynamics to enhance fear representation.
    Article Snippet: Loss function: negative log-likelihood loss Optimization method: Adam Using the Matlab ‘‘fitcnb’’ function, we fitted a naı̈ve Bayesian classifier to normalized prediction scores of CFC1 and CFC2 (2 data points per animal), and obtained the corresponding fitted probability density functions.

    Article Title: Ventral Hippocampal CA1 Pyramidal Neurons Encode Nociceptive Information.
    Article Snippet: Naïve Bayes classifiers in the MATLAB programming environment (fitcnb) were applied to test the specificity of the vCA1 neural code in response to various stimuli [7].

    Activity Assay:

    Article Title: A stable hippocampal code in freely flying bats
    Article Snippet: .. We devised a constrained naive Bayesian decoder 57 to predict the flight path membership from neural activity, using the fitcnb function in MATLAB ( , ). ..



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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).
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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: Naive Bayes , nb , Matlab , Function fitcnb.

    Techniques: Plasmid Preparation