# resubPredict

Predict resubstitution response of classifier

## 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 How the predict Method Classifies. `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.

## Definitions

### Posterior Probability

`posterior(i,k)` is the posterior probability of class `k` for observation `i`. For the mathematical definition, see 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```