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classification-learner package  (MathWorks Inc)


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    MathWorks Inc classification-learner package
    Characteristics, classification and objectives of the machine learning models used in the studies
    Classification Learner Package, 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/classification-learner+package/pmc11843972-61-27-27
    Average 90 stars, based on 1 article reviews
    classification-learner package - by Bioz Stars, 2026-09
    90/100 stars

    Images

    1) Product Images from "Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review"

    Article Title: Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review

    Journal: BMC Medical Research Methodology

    doi: 10.1186/s12874-025-02463-y

    Characteristics, classification and objectives of the machine learning models used in the studies
    Figure Legend Snippet: Characteristics, classification and objectives of the machine learning models used in the studies

    Techniques Used: Software, Biomarker Discovery, Diagnostic Assay, Imaging, Staining

    Related Articles

    Biomarker Discovery:

    Article Title: Estimating the likelihood of epilepsy from clinically noncontributory electroencephalograms using computational analysis: A retrospective, multisite case-control study.
    Article Snippet: .. Data were partitioned using nested 10- fold cross- validation, and a suite of statistical models were trained using MATLAB's Classification Learner application (Figure 2). ..

    other:

    Article Title: Forecasting cryptocurrency's buy signal with a bagged tree learning approach to enhance purchase decisions
    Article Snippet: The maximum number of splits in the training can reach up to 335,662 because, in a bagged tree, the default maximum number of splits reaches the training data size in MATLAB's Classification Learner app.

    Article Title: Predictive analysis of economic and clinical outcomes in total knee arthroplasty: Identifying high-risk patients for increased costs and length of stay.
    Article Snippet: David Maman, Department of Orthopaedic Surgery, Carmel Medical Center, 7 Michal St., Haifa 3436212, Israel.. Email: maman.david@technion.ac.il Abstract Purpose: The purpose of this study was to predict high‐risk patients who experience significant increases in hospital charges and length of stay (LOS) following specific postoperative complications.. Methods: This study analyzed over two million patients from the Nationwide Inpatient Sample database undergoing elective total knee arthroplasty (TKA) for primary osteoarthritis.

    Article Title: Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review
    Article Snippet: Varghese [ ] b , Incidence risk stratification (classification): QSVM (radiomics-based) , Risk stratification for prostate cancer in low- and high-risk patients , desktop-based , CDSS , MATLAB's Classification-Learner Package , NR , NR.

    Article Title: Habitat suitability of biocrust communities in a cold desert ecosystem.
    Article Snippet: MATLAB's classification learner tested 40+ different models (e.g., decision trees, support vector machines, nearest neighbors, Bayes classifiers, ensemble classifiers, neural networks) to assess whether our data could be successfully trained to classify data, as did MATLAB's regression learner (e.g., linear regression, regression trees, support vector machines, kernel approximation, ensembles of trees, neural networks), assessing whether our data could train regression models for prediction.



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    Image Search Results


    Characteristics, classification and objectives of the machine learning models used in the studies

    Journal: BMC Medical Research Methodology

    Article Title: Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review

    doi: 10.1186/s12874-025-02463-y

    Figure Lengend Snippet: Characteristics, classification and objectives of the machine learning models used in the studies

    Article Snippet: Varghese [ ] b , Incidence risk stratification (classification): QSVM (radiomics-based) , Risk stratification for prostate cancer in low- and high-risk patients , desktop-based , CDSS , MATLAB's Classification-Learner Package , NR , NR.

    Techniques: Software, Biomarker Discovery, Diagnostic Assay, Imaging, Staining