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tree classifier matlab classification learner app  (MathWorks Inc)


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    MathWorks Inc tree classifier matlab classification learner app
    Tree Classifier Matlab Classification Learner App, 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/matlab+classifier+learner/pm40592988-316-41-43
    Average 90 stars, based on 1 article reviews
    tree classifier matlab classification learner app - by Bioz Stars, 2026-09
    90/100 stars

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    Article Title: A comprehensive guide to selecting suitable wavelet decomposition level and functions in discrete wavelet transform for fault detection in distribution networks
    Article Snippet: The significant variation in the max absolute values of current detailed coefficient), and Sum of absolute values of current detailed coefficient of sym3 at Level 8 that indicate the variance in higher frequency bands during fault inception, this index is used to train and validate the machine learning classifier based on MATLAB Classifier Learner, to selecting the most appropriate mother wavelet.

    Article Title: A comprehensive guide to selecting suitable wavelet decomposition level and functions in discrete wavelet transform for fault detection in distribution networks
    Article Snippet: Finally, Using MATLAB Classifier Learner (MLC) to select the appropriate wavelet function for the classifier based on higher accuracy, faster prediction speed, and reduced training time.

    Article Title: Design and Validation of a Portable Machine Learning-Based Electronic Nose
    Article Snippet: C,D display the schematic diagram of the EMLA unit, 3D-printed platform, and sample holders. displays the survey of the classifiers conducted on the Matlab classifier learner while shows an example of training model from linear SVM.



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    Diagnostic performance evaluation of the proposed CAD system using different machine learning classifiers provided by the MATLAB 2017 Tool Box such that Acc: accuracy, Sens: sensitivity, Spec: specificity, and AUC: area under the curve.

    Journal: Proceedings. International Conference on Image Processing

    Article Title: EARLY ASSESSMENT OF RENAL TRANSPLANTS USING BOLD-MRI: PROMISING RESULTS

    doi: 10.1109/ICIP.2019.8803042

    Figure Lengend Snippet: Diagnostic performance evaluation of the proposed CAD system using different machine learning classifiers provided by the MATLAB 2017 Tool Box such that Acc: accuracy, Sens: sensitivity, Spec: specificity, and AUC: area under the curve.

    Article Snippet: The matrix of global features of size 15 × 4 of mean R2* values at 7, 12, 17, and 22 ms were used with a LOOCV approach to train and test 8 different classifiers provided by MATLAB 2017 classification learner Tool Box (random forest (RF), linear discriminant analysis (LDA), logistic regression (logR), quadratic SVM (SVM Quad ), cubic SVM (SVM Cub ), radial basis function SVM ((SVM RBF ), ensemble bagged trees (EBT), and ANNs).

    Techniques: Diagnostic Assay