# predict

Predict labels using classification ensemble model

## Description

specifies additional options using one or more name-value arguments. For example,
you can specify the weak learners to use for predictions, and perform computations
in parallel. `labels`

= predict(`ens`

,`X`

,`Name=Value`

)

`[`

also returns a matrix of classification scores indicating the likelihood that a label comes from a particular
class, using any of the input argument combinations in the previous syntaxes. For
each observation in `labels`

,`scores`

]
= predict(___)`X`

, the predicted class label corresponds to
the maximum score among all classes.

## Examples

### Predict Class Labels Using Classification Ensemble

Load Fisher's iris data set. Determine the sample size.

```
load fisheriris
N = size(meas,1);
```

Partition the data into training and test sets. Hold out 10% of the data for testing.

rng(1); % For reproducibility cvp = cvpartition(N,'Holdout',0.1); idxTrn = training(cvp); % Training set indices idxTest = test(cvp); % Test set indices

Store the training data in a table.

tblTrn = array2table(meas(idxTrn,:)); tblTrn.Y = species(idxTrn);

Train a classification ensemble using AdaBoostM2 and the training set. Specify tree stumps as the weak learners.

t = templateTree('MaxNumSplits',1); Mdl = fitcensemble(tblTrn,'Y','Method','AdaBoostM2','Learners',t);

Predict labels for the test set. You trained model using a table of data, but you can predict labels using a matrix.

labels = predict(Mdl,meas(idxTest,:));

Construct a confusion matrix for the test set.

confusionchart(species(idxTest),labels)

`Mdl`

misclassifies one versicolor iris as virginica in the test set.

### Assess Performance of Ensemble of Boosted Trees

Create an ensemble of boosted trees and inspect the importance of each predictor. Using test data, assess the classification accuracy of the ensemble.

Load the arrhythmia data set. Determine the class representations in the data.

```
load arrhythmia
Y = categorical(Y);
tabulate(Y)
```

Value Count Percent 1 245 54.20% 2 44 9.73% 3 15 3.32% 4 15 3.32% 5 13 2.88% 6 25 5.53% 7 3 0.66% 8 2 0.44% 9 9 1.99% 10 50 11.06% 14 4 0.88% 15 5 1.11% 16 22 4.87%

The data set contains 16 classes, but not all classes are represented (for example, class 13). Most observations are classified as not having arrhythmia (class 1). The data set is highly discrete with imbalanced classes.

Combine all observations with arrhythmia (classes 2 through 15) into one class. Remove those observations with an unknown arrhythmia status (class 16) from the data set.

idx = (Y ~= "16"); Y = Y(idx); X = X(idx,:); Y(Y ~= "1") = "WithArrhythmia"; Y(Y == "1") = "NoArrhythmia"; Y = removecats(Y);

Create a partition that evenly splits the data into training and test sets.

rng("default") % For reproducibility cvp = cvpartition(Y,"Holdout",0.5); idxTrain = training(cvp); idxTest = test(cvp);

`cvp`

is a cross-validation partition object that specifies the training and test sets.

Train an ensemble of 100 boosted classification trees using `AdaBoostM1`

. Specify to use tree stumps as the weak learners. Also, because the data set contains missing values, specify to use surrogate splits.

t = templateTree("MaxNumSplits",1,"Surrogate","on"); numTrees = 100; mdl = fitcensemble(X(idxTrain,:),Y(idxTrain),"Method","AdaBoostM1", ... "NumLearningCycles",numTrees,"Learners",t);

`mdl`

is a trained `ClassificationEnsemble`

model.

Inspect the importance measure for each predictor.

predImportance = predictorImportance(mdl); bar(predImportance) title("Predictor Importance") xlabel("Predictor") ylabel("Importance Measure")

Identify the top ten predictors in terms of their importance.

```
[~,idxSort] = sort(predImportance,"descend");
idx10 = idxSort(1:10)
```

`idx10 = `*1×10*
228 233 238 93 15 224 91 177 260 277

Classify the test set observations. View the results using a confusion matrix. Blue values indicate correct classifications, and red values indicate misclassified observations.

predictedValues = predict(mdl,X(idxTest,:)); confusionchart(Y(idxTest),predictedValues)

Compute the accuracy of the model on the test data.

error = loss(mdl,X(idxTest,:),Y(idxTest), ... "LossFun","classiferror"); accuracy = 1 - error

accuracy = 0.7731

`accuracy`

estimates the fraction of correctly classified observations.

## Input Arguments

`ens`

— Classification ensemble model

`ClassificationEnsemble`

model object | `CompactClassificationEnsemble`

model object

Full classification ensemble model, specified as a `ClassificationEnsemble`

model object trained with `fitcensemble`

, or a `CompactClassificationEnsemble`

model object created with `compact`

.

`X`

— Predictor data

numeric matrix | table

Predictor data to be classified, specified as a numeric matrix or a table.

Each row of `X`

corresponds to one observation, and each
column corresponds to one variable.

For a numeric matrix:

The variables that make up the columns of

`X`

must have the same order as the predictor variables used to train`ens`

.If you trained

`ens`

using a table (for example,`tbl`

),`X`

can be a numeric matrix if`tbl`

contains only numeric predictor variables. To treat numeric predictors in`tbl`

as categorical during training, specify categorical predictors using the`CategoricalPredictors`

name-value argument of`fitcensemble`

. If`tbl`

contains heterogeneous predictor variables (for example, numeric and categorical data types) and`X`

is a numeric matrix,`predict`

issues an error.

For a table:

`predict`

does not support multicolumn variables or cell arrays other than cell arrays of character vectors.If you trained

`ens`

using a table (for example,`tbl`

), all predictor variables in`X`

must have the same variable names and data types as those used to train`ens`

(stored in`ens.PredictorNames`

). However, the column order of`X`

does not need to correspond to the column order of`tbl`

.`tbl`

and`X`

can contain additional variables, such as response variables and observation weights, but`predict`

ignores them.If you trained

`ens`

using a numeric matrix, then the predictor names in`ens.PredictorNames`

must be the same as the corresponding predictor variable names in`X`

. To specify predictor names during training, use the`PredictorNames`

name-value argument of`fitcensemble`

. All predictor variables in`X`

must be numeric vectors.`X`

can contain additional variables, such as response variables and observation weights, but`predict`

ignores them.

### 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: **`predict(ens,X,Learners=[1 2 3 5],UseParallel=true)`

specifies to use the first, second, third, and fifth learners in the ensemble
`ens`

, and to perform computations in
parallel.

`Learners`

— Indices of weak learners

`[1:ens.NumTrained]`

(default) | vector of positive integers

Indices of weak learners in the ensemble to use in
`predict`

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

]. By default, all learners are used.

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

**Data Types: **`single`

| `double`

`UseObsForLearner`

— Option to use observations for learners

`true(N,T)`

(default) | logical matrix

Option to use observations for learners, specified as a logical matrix of size
`N`

-by-`T`

, where:

When `UseObsForLearner(i,j)`

is `true`

(default),
learner `j`

is used in predicting the class of row `i`

of `X`

.

**Example: **`UseObsForLearner=logical([1 1; 0 1; 1 0])`

**Data Types: **`logical matrix`

`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
`predict`

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 array | numeric array | cell array of character vectors

Predicted class labels, returned as a categorical, character, logical, or
numeric array, or a cell array of character vectors.
`labels`

has the same data type as the labels used to
train `ens`

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

The `predict`

function classifies an observation into the class yielding the highest score. 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.

`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. For more information, see Score (ensemble).

## More About

### Score (ensemble)

For ensembles, a classification *score* represents the
confidence that an observation originates from a specific 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 ensemble type. For example:

`Bag`

scores range from`0`

to`1`

. You can interpret these scores as probabilities averaged over all the trees in the ensemble.`AdaBoostM1`

,`GentleBoost`

, and`LogitBoost`

scores range from –∞ to ∞. You can convert these scores to probabilities by setting the`ScoreTransform`

property of`ens`

to`"doublelogit"`

before passing`ens`

to`predict`

:Alternatively, you can specify`ens.ScoreTransform = "doublelogit"; [labels,scores] = predict(ens,X);`

`ScoreTransform="doublelogit"`

in the call to`fitcensemble`

when you create`ens`

.

For more information on the different ensemble algorithms and how they compute scores, see Ensemble Algorithms.

## Alternative Functionality

### Simulink Block

To integrate the prediction of an ensemble into Simulink^{®}, you can use the ClassificationEnsemble Predict block in the Statistics and Machine Learning Toolbox™ library or a MATLAB^{®} Function block with the `predict`

function. For
examples, see Predict Class Labels Using ClassificationEnsemble Predict Block and Predict Class Labels Using MATLAB Function Block.

When deciding which approach to use, consider the following:

If you use the Statistics and Machine Learning Toolbox library block, you can use the Fixed-Point Tool (Fixed-Point Designer) to convert a floating-point model to fixed point.

Support for variable-size arrays must be enabled for a MATLAB Function block with the

`predict`

function.If you use a MATLAB Function block, you can use MATLAB functions for preprocessing or post-processing before or after predictions in the same MATLAB Function block.

## Extended Capabilities

### Tall Arrays

Calculate with arrays that have more rows than fit in memory.

Usage notes and limitations:

You cannot use the

`UseParallel`

name-value argument with tall arrays.

For more information, see Tall Arrays.

### C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

Use

`saveLearnerForCoder`

,`loadLearnerForCoder`

, and`codegen`

(MATLAB Coder) to generate code for the`predict`

function. Save a trained model by using`saveLearnerForCoder`

. Define an entry-point function that loads the saved model by using`loadLearnerForCoder`

and calls the`predict`

function. Then use`codegen`

to generate code for the entry-point function.To generate single-precision C/C++ code for

`predict`

, specify the name-value argument`"DataType","single"`

when you call the`loadLearnerForCoder`

function.You can also generate fixed-point C/C++ code for

`predict`

. Fixed-point code generation requires an additional step that defines the fixed-point data types of the variables required for prediction. Create a fixed-point data type structure by using the data type function generated by`generateLearnerDataTypeFcn`

, and then use the structure as an input argument of`loadLearnerForCoder`

in an entry-point function. Generating fixed-point C/C++ code requires MATLAB Coder™ and Fixed-Point Designer™.Generating fixed-point code for

`predict`

includes propagating data types for individual learners and, therefore, can be time consuming.This table contains notes about the arguments of

`predict`

. Arguments not included in this table are fully supported.Argument Notes and Limitations `ens`

For the usage notes and limitations of the model object, see Code Generation of the

`CompactClassificationEnsemble`

object.`X`

For general code generation,

`X`

must be a single-precision or double-precision matrix or a table containing numeric variables, categorical variables, or both.For fixed-point code generation,

`X`

must be a fixed-point matrix.The number of rows, or observations, in

`X`

can be a variable size, but the number of columns in`X`

must be fixed.If you want to specify

`X`

as a table, then your model must be trained using a table, and your entry-point function for prediction must do the following:Accept data as arrays.

Create a table from the data input arguments and specify the variable names in the table.

Pass the table to

`predict`

.

For an example of this table workflow, see Generate Code to Classify Data in Table. For more information on using tables in code generation, see Code Generation for Tables (MATLAB Coder) and Table Limitations for Code Generation (MATLAB Coder).

Name-value arguments Names in name-value arguments must be compile-time constants. For example, to allow user-defined indices up to 5 weak learners in the generated code, include

`{coder.Constant('Learners'),coder.typeof(0,[1,5],[0,1])}`

in the`-args`

value of`codegen`

(MATLAB Coder).`"Learners"`

For fixed-point code generation, the

`"Learners"`

value must have an integer data type.

For more information, see Introduction to Code Generation.

### 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).

You cannot use `UseParallel`

with tall or GPU arrays or in code generation.

### GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Usage notes and limitations:

The

`predict`

function does not support ensembles trained using decision tree learners with surrogate splits.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

## Version History

**Introduced in R2011a**

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