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Label new data using semi-supervised self-trained classifier



    label = predict(Mdl,X) returns a vector of predicted class labels for the data in the table or matrix X, based on the semi-supervised self-trained classifier Mdl.

    [label,score] = predict(Mdl,X) also returns a matrix of scores indicating the likelihood that a label comes from a particular class. For each observation in X, the predicted class label corresponds to the maximum score among all classes.


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    Use both labeled and unlabeled data to train a SemiSupervisedSelfTrainingModel object. Label new data using the trained model.

    Randomly generate 15 observations of labeled data, with 5 observations in each of three classes.

    rng('default') % For reproducibility
    labeledX = [randn(5,2)*0.25 + ones(5,2);
                randn(5,2)*0.25 - ones(5,2);
    Y = [ones(5,1); ones(5,1)*2; ones(5,1)*3];

    Randomly generate 300 additional observations of unlabeled data, with 100 observations per class.

    unlabeledX = [randn(100,2)*0.25 + ones(100,2);
                  randn(100,2)*0.25 - ones(100,2);

    Fit labels to the unlabeled data by using a semi-supervised self-training method. The function fitsemiself returns a SemiSupervisedSelfTrainingModel object whose FittedLabels property contains the fitted labels for the unlabeled data and whose LabelScores property contains the associated label scores.

    Mdl = fitsemiself(labeledX,Y,unlabeledX)
    Mdl = 
      SemiSupervisedSelfTrainingModel with properties:
                 FittedLabels: [300x1 double]
                  LabelScores: [300x3 double]
                   ClassNames: [1 2 3]
                 ResponseName: 'Y'
        CategoricalPredictors: []
                      Learner: [1x1 classreg.learning.classif.CompactClassificationECOC]
      Properties, Methods

    Randomly generate 150 observations of new data, with 50 observations per class. For the purposes of validation, keep track of the true labels for the new data.

    newX = [randn(50,2)*0.25 + ones(50,2);
            randn(50,2)*0.25 - ones(50,2);
    trueLabels = [ones(50,1); ones(50,1)*2; ones(50,1)*3];

    Predict the labels for the new data by using the predict function of the SemiSupervisedSelfTrainingModel object. Compare the true labels to the predicted labels by using a confusion matrix.

    predictedLabels = predict(Mdl,newX);

    Figure contains an object of type ConfusionMatrixChart.

    Only 8 of the 150 observations in newX are mislabeled.

    Input Arguments

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    Semi-supervised self-training classifier, specified as a SemiSupervisedSelfTrainingModel object returned by fitsemiself.

    Predictor data to be classified, specified as a numeric matrix or table. Each row of X corresponds to one observation, and each column corresponds to one variable.

    If you trained Mdl using matrix data (X and UnlabeledX in the call to fitsemiself), then specify X as a numeric matrix.

    • The variables in the columns of X must have the same order as the predictor variables that trained Mdl.

    • The software treats the predictors in X whose indices match Mdl.CategoricalPredictors as categorical predictors.

    If you trained Mdl using tabular data (Tbl and UnlabeledTbl in the call to fitsemiself), then specify X as a table.

    • All predictor variables in X must have the same variable names and data types as those that trained Mdl (stored in Mdl.PredictorNames). However, the column order of X does not need to correspond to the column order of Tbl. Also, Tbl and X can contain additional variables (for example, response variables), but predict ignores them.

    • predict does not support multicolumn variables or cell arrays other than cell arrays of character vectors.

    Data Types: single | double | table

    Output Arguments

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    Predicted class labels, returned as a categorical or character array, logical or numeric vector, or cell array of character vectors. label has the same data type as the fitted class labels Mdl.FittedLabels, and its length is equal to the number of rows in X.

    Predicted class scores, returned as a numeric matrix. score has size m-by-K, where m is the number of observations (or rows) in X and K is the number of classes in Mdl.ClassNames.

    score(m,k) is the likelihood that observation m in X belongs to class k, where a higher score value indicates a higher likelihood. The range of score values depends on the underlying classifier Mdl.Learner.

    Introduced in R2020b