Documentation

### This is machine translation

Mouseover text to see original. Click the button below to return to the English version of the page.

Note: This page has been translated by MathWorks. Click here to see
To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

# kfoldPredict

Predict response for observations not used for training

## Syntax

```label = kfoldPredict(obj) [label,score] = kfoldPredict(obj) [label,score,cost] = kfoldPredict(obj) ```

## Description

`label = kfoldPredict(obj)` returns class labels predicted by `obj`, a cross-validated classification. For every fold, `kfoldPredict` predicts class labels for in-fold observations using a model trained on out-of-fold observations.

```[label,score] = kfoldPredict(obj)``` returns the predicted classification scores for in-fold observations using a model trained on out-of-fold observations.

```[label,score,cost] = kfoldPredict(obj)``` returns misclassification costs.

## Input Arguments

 `obj`

## Output Arguments

 `label` Vector of class labels of the same type as the response data used in training `obj`. (The software treats string arrays as cell arrays of character vectors.) Each entry of `label` corresponds to a predicted class label for the corresponding row of `X`. `score` Numeric matrix of size `N`-by-`K`, where `N` is the number of observations (rows) in `obj``.X`, and `K` is the number of classes (in `obj.ClassNames`). `score(i,j)` represents the confidence that row `i` of `obj``.X` is of class `j`. For details, see Definitions. `cost` Numeric matrix of misclassification costs of size `N`-by-`K`. `cost(i,j)` is the average misclassification cost of predicting that row `i` of `obj``.X` is of class `j`.

## Examples

expand all

Find the cross-validation predictions for a model based on Fisher's iris data.

Load Fisher's iris data set.

`load fisheriris`

Train an ensemble of classification trees using AdaBoostM2. Specify tree stumps as the weak learners.

```rng(1); % For reproducibility t = templateTree('MaxNumSplits',1); Mdl = fitcensemble(meas,species,'Method','AdaBoostM2','Learners',t);```

Cross validate the trained ensemble using 10-fold cross validation.

`CVMdl = crossval(Mdl);`

Estimate cross-validation predicted labels and scores.

`[elabel,escore] = kfoldPredict(CVMdl);`

Display the maximum and minimum scores of each class.

`max(escore)`
```ans = 1×3 9.3862 8.9871 10.1866 ```
`min(escore)`
```ans = 1×3 0.0018 3.8359 0.9573 ```

expand all

Download ebook