# oobPredict

Predict out-of-bag labels and scores of bagged classification ensemble

## Description

`[`

specifies additional options using one or more name-value arguments. For example,
you can specify the indices of the weak learners to use for calculating the
predicted labels.`labels`

,`scores`

]
= oobPredict(`ens`

,`Name=Value`

)

## Examples

### Find Out-of-Bag Response of Classification Ensemble

Find the out-of-bag predictions and scores for the Fisher iris data. Find the scores with notable uncertainty in the resulting classifications.

Load the sample data set.

`load fisheriris`

Train an ensemble of bagged classification trees.

ens = fitcensemble(meas,species,'Method','Bag');

Find the out-of-bag predictions and scores.

[label,score] = oobPredict(ens);

Find the scores in the range `(0.2,0.8)`

. These scores have notable uncertainty in the resulting classifications.

```
unsure = ((score > .2) & (score < .8));
sum(sum(unsure)) % Number of uncertain predictions
```

ans = 16

## Input Arguments

`ens`

— Bagged classification ensemble model

`ClassificationBaggedEnsemble`

model object

Bagged classification ensemble model, specified as a `ClassificationBaggedEnsemble`

model object trained with `fitcensemble`

.

### Name-Value Arguments

Specify optional pairs of arguments as
`Name1=Value1,...,NameN=ValueN`

, where `Name`

is
the argument name and `Value`

is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.

*
Before R2021a, use commas to separate each name and value, and enclose*
`Name`

*in quotes.*

**Example: **`oobPredict(ens,Learners=[1 2 3 5],UseParallel=true)`

specifies to use the first, second, third, and fifth learners in the ensemble, and
to perform computations in parallel.

`Learners`

— Indices of weak learners

`[1:ens.NumTrained]`

(default) | vector of positive integers

Indices of the weak learners in the ensemble to use with
`oobPredict`

, specified as a
vector of positive integers in the range
[1:`ens.NumTrained`

]. By default,
the function uses all learners.

**Example: **`Learners=[1 2 4]`

**Data Types: **`single`

| `double`

`UseParallel`

— Flag to run in parallel

`false`

or
`0`

(default) | `true`

or `1`

Flag to run in parallel, specified as a numeric or logical 1
(`true`

) or 0 (`false`

). If you specify
`UseParallel=true`

, the `oobPredict`

function executes
`for`

-loop iterations by using `parfor`

. The loop runs in parallel when you have Parallel Computing Toolbox™.

**Example: **`UseParallel=true`

**Data Types: **`logical`

## Output Arguments

`labels`

— 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.

For each observation in `X`

, the predicted class label
corresponds to the minimum expected classification cost among all classes.
For an observation with `NaN`

scores, the
function classifies the observation into the majority class, which makes up the largest
proportion of the training labels.

The label is the class with the highest score. In case of a tie, the label is earliest in

`ens`

`.ClassNames`

.`labels`

has the same data type as the observed class labels (`Y`

) used to train`ens`

. (The software treats string arrays as cell arrays of character vectors.)The length of

`labels`

is equal to the number of rows of`ens.X`

.

`scores`

— Class scores

numeric matrix

Class scores, returned as a numeric matrix with one row per observation
and one column per class. For each observation and each class, the score
represents the confidence that the observation originates from that class. A
higher score indicates a higher confidence. Score values are in the range
`0`

to `1`

. For more information, see
Score (ensemble).

## More About

### Out of Bag

*Bagging*, which stands for “bootstrap aggregation,”, is a
type of ensemble learning. To bag a weak learner such as a decision tree on a data set,
`fitcensemble`

generates many bootstrap
replicas of the data set and grows decision trees on these replicas. `fitcensemble`

obtains each bootstrap replica by randomly selecting
`N`

observations out of `N`

with replacement, where
`N`

is the data set size. To find the predicted response of a trained
ensemble, `predict`

take an average over predictions from
individual trees.

Drawing `N`

out of `N`

observations
with replacement omits on average 37% (1/*e*) of
observations for each decision tree. These are "out-of-bag" observations.
For each observation, `oobLoss`

estimates the out-of-bag
prediction by averaging over predictions from all trees in the ensemble
for which this observation is out of bag. It then compares the computed
prediction against the true response for this observation. It calculates
the out-of-bag error by comparing the out-of-bag predicted responses
against the true responses for all observations used for training.
This out-of-bag average is an unbiased estimator of the true ensemble
error.

### Score (ensemble)

For ensembles, a classification *score* represents the
confidence of a classification into a class. The higher the score, the higher the
confidence.

Different ensemble algorithms have different definitions for their scores. Furthermore, the range of scores depends on the ensemble type. For example:

`AdaBoostM1`

scores range from –∞ to ∞.`Bag`

scores range from`0`

to`1`

.

## Extended Capabilities

### Automatic Parallel Support

Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.

To run in parallel, set the `UseParallel`

name-value argument to
`true`

in the call to this function.

For more general information about parallel computing, see Run MATLAB Functions with Automatic Parallel Support (Parallel Computing Toolbox).

## Version History

**Introduced in R2012b**

## See Also

`oobMargin`

| `oobLoss`

| `oobEdge`

| `predict`

| `ClassificationBaggedEnsemble`

| `fitcensemble`

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