resubPredict

Predict resubstitution labels of discriminant analysis classification model

Syntax

```label = resubPredict(obj) [label,posterior] = resubPredict(obj) [label,posterior,cost] = resubPredict(obj) ```

Description

`label = resubPredict(obj)` returns the labels `obj` predicts for the data `obj.X`. `label` is the predictions of `obj` on the data that `fitcdiscr` used to create `obj`.

```[label,posterior] = resubPredict(obj)``` returns the posterior class probabilities for the predictions.

```[label,posterior,cost] = resubPredict(obj)``` returns the predicted misclassification costs per class for the resubstituted data.

Input Arguments

 `obj` Discriminant analysis classifier, produced using `fitcdiscr`.

Output Arguments

 `label` Response `obj` predicts for the training data. `label` is the same data type as the training response data `obj.Y`. The predicted class labels are those with minimal expected misclassification cost; see Prediction Using Discriminant Analysis Models. `posterior` `N`-by-`K` matrix of posterior probabilities for classes `obj` predicts, where `N` is the number of observations and `K` is the number of classes. `cost` `N`-by-`K` matrix of predicted misclassification costs. Each cost is the average misclassification cost with respect to the posterior probability.

Examples

Find the total number of misclassifications of the Fisher iris data for a discriminant analysis classifier:

```load fisheriris obj = fitcdiscr(meas,species); Ypredict = resubPredict(obj); % the predictions Ysame = strcmp(Ypredict,species); % true when == sum(~Ysame) % how many are different? ans = 3```