label = resubPredict(Mdl)
returns a vector of predicted class labels for the trained discriminant analysis classifier
Mdl using the predictor data stored in Mdl.X.
label has the same data type as the training response data
Mdl.Y, and the same number of entries as the number of rows in
Mdl.X.
label — Predicted class labels categorical array | character array | logical vector | numeric vector | cell array of character vectors
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
Mdl.ClassNames and the same number of rows as
Mdl.X. The predicted class labels are those with minimal expected
misclassification cost. See Prediction Using Discriminant Analysis Models.
posterior — Posterior probabilities for classes predicted by Mdl N-by-K matrix
Posterior probabilities for classes predicted by Mdl, returned as
an N-by-K matrix. N is the
number of observations, and K is the number of classes.
Predicted misclassification costs, returned as an
N-by-K matrix. N is the
number of observations, and K is the number of classes. Each cost is
the average misclassification cost with respect to the posterior probability.
R2023b: Observations with missing predictor values are used in resubstitution and cross-validation computations
Starting in R2023b, the following classification model object functions use observations with
missing predictor values as part of resubstitution ("resub") and cross-validation ("kfold")
computations for classification edges, losses, margins, and predictions.
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