# predict

Predict labels for Gaussian kernel classification model

## Description

## Examples

### Predict Training Set Labels

Predict the training set labels using a binary kernel classification model, and display the confusion matrix for the resulting classification.

Load the `ionosphere`

data set. This data set has 34 predictors and 351 binary responses for radar returns, either bad (`'b'`

) or good (`'g'`

).

`load ionosphere`

Train a binary kernel classification model that identifies whether the radar return is bad (`'b'`

) or good (`'g'`

).

rng('default') % For reproducibility Mdl = fitckernel(X,Y);

`Mdl`

is a `ClassificationKernel`

model.

Predict the training set, or resubstitution, labels.

label = predict(Mdl,X);

Construct a confusion matrix.

ConfusionTrain = confusionchart(Y,label);

The model misclassifies one radar return for each class.

### Predict Test Set Labels

Predict the test set labels using a binary kernel classification model, and display the confusion matrix for the resulting classification.

Load the `ionosphere`

data set. This data set has 34 predictors and 351 binary responses for radar returns, either bad (`'b'`

) or good (`'g'`

).

`load ionosphere`

Partition the data set into training and test sets. Specify a 15% holdout sample for the test set.

rng('default') % For reproducibility Partition = cvpartition(Y,'Holdout',0.15); trainingInds = training(Partition); % Indices for the training set testInds = test(Partition); % Indices for the test set

Train a binary kernel classification model using the training set. A good practice is to define the class order.

Mdl = fitckernel(X(trainingInds,:),Y(trainingInds),'ClassNames',{'b','g'});

Predict the training-set labels and the test set labels.

labelTrain = predict(Mdl,X(trainingInds,:)); labelTest = predict(Mdl,X(testInds,:));

Construct a confusion matrix for the training set.

ConfusionTrain = confusionchart(Y(trainingInds),labelTrain);

The model misclassifies only one radar return for each class.

Construct a confusion matrix for the test set.

ConfusionTest = confusionchart(Y(testInds),labelTest);

The model misclassifies one bad radar return as being a good return, and five good radar returns as being bad returns.

### Estimate Posterior Class Probabilities

Estimate posterior class probabilities for a test set, and determine the quality of the model by plotting a receiver operating characteristic (ROC) curve. Kernel classification models return posterior probabilities for logistic regression learners only.

Load the `ionosphere`

data set. This data set has 34 predictors and 351 binary responses for radar returns, either bad (`'b'`

) or good (`'g'`

).

`load ionosphere`

Partition the data set into training and test sets. Specify a 30% holdout sample for the test set.

rng('default') % For reproducibility Partition = cvpartition(Y,'Holdout',0.30); trainingInds = training(Partition); % Indices for the training set testInds = test(Partition); % Indices for the test set

Train a binary kernel classification model. Fit logistic regression learners.

Mdl = fitckernel(X(trainingInds,:),Y(trainingInds), ... 'ClassNames',{'b','g'},'Learner','logistic');

Predict the posterior class probabilities for the test set.

[~,posterior] = predict(Mdl,X(testInds,:));

Because `Mdl`

has one regularization strength, the output `posterior`

is a matrix with two columns and rows equal to the number of test-set observations. Column `i`

contains posterior probabilities of `Mdl.ClassNames(i)`

given a particular observation.

Compute the performance metrics (true positive rates and false positive rates) for a ROC curve and find the area under the ROC curve (AUC) value by creating a `rocmetrics`

object.

rocObj = rocmetrics(Y(testInds),posterior,Mdl.ClassNames);

Plot the ROC curve for the second class by using the `plot`

function of `rocmetrics`

.

plot(rocObj,ClassNames=Mdl.ClassNames(2))

The AUC is close to `1`

, which indicates that the model predicts labels well.

## Input Arguments

`Mdl`

— Binary kernel classification model

`ClassificationKernel`

model object

Binary kernel classification model, specified as a `ClassificationKernel`

model object. You can create a
`ClassificationKernel`

model object using `fitckernel`

.

`X`

— Predictor data to be classified

numeric matrix | table

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

Each row of `X`

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

For a numeric matrix:

The variables in the columns of

`X`

must have the same order as the predictor variables that trained`Mdl`

.If you trained

`Mdl`

using a table (for example,`Tbl`

) and`Tbl`

contains all numeric predictor variables, then`X`

can be a numeric matrix. To treat numeric predictors in`Tbl`

as categorical during training, identify categorical predictors by using the`CategoricalPredictors`

name-value pair argument of`fitckernel`

. If`Tbl`

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

is a numeric matrix, then`predict`

throws an error.

For a table:

`predict`

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

`Mdl`

using a table (for example,`Tbl`

), then all predictor variables in`X`

must have the same variable names and data types as those that trained`Mdl`

(stored in`Mdl.PredictorNames`

). However, the column order of`X`

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

. Also,`Tbl`

and`X`

can contain additional variables (response variables, observation weights, and so on), but`predict`

ignores them.If you trained

`Mdl`

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

and corresponding predictor variable names in`X`

must be the same. To specify predictor names during training, see the`PredictorNames`

name-value pair argument of`fitckernel`

. All predictor variables in`X`

must be numeric vectors.`X`

can contain additional variables (response variables, observation weights, and so on), but`predict`

ignores them.

**Data Types: **`table`

| `double`

| `single`

## Output Arguments

`Label`

— Predicted class labels

categorical array | character array | logical matrix | numeric matrix | cell array of character vectors

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

`Label`

has *n* rows, where
*n* is the number of observations in
`X`

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

) used to train `Mdl`

.
(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.

`Score`

— Classification scores

numeric array

Classification scores, returned as an *n*-by-2
numeric array, where *n* is the number of observations in
`X`

.
`Score(`

is the score for classifying observation * i*,

*)*

`j`

*into class*

`i`

*.*

`j`

`Mdl.ClassNames`

stores
the order of the classes.If `Mdl.Learner`

is `'logistic'`

, then
classification scores are posterior probabilities.

## More About

### Classification Score

For kernel classification models, the raw *classification
score* for classifying the observation *x*, a row vector,
into the positive class is defined by

$$f\left(x\right)=T(x)\beta +b.$$

$$T(\xb7)$$ is a transformation of an observation for feature expansion.

*β*is the estimated column vector of coefficients.*b*is the estimated scalar bias.

The raw classification score for classifying *x* into the negative class is −*f*(*x*). The software classifies observations into the class that yields a
positive score.

If the kernel classification model consists of logistic regression learners, then the
software applies the `'logit'`

score transformation to the raw
classification scores (see `ScoreTransform`

).

## Extended Capabilities

### Tall Arrays

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

The
`predict`

function supports tall arrays with the following usage
notes and limitations:

`predict`

does not support tall`table`

data.

For more information, see Tall Arrays.

### C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™. (since R2023a)

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.If the code generator uses the Open Multiprocessing (OpenMP) library, the generated code of

`predict`

splits the predictor data`X`

into multiple chunks and predicts responses for the chunks in parallel. The generated code uses`parfor`

(MATLAB Coder) to create loops that run in parallel on supported shared-memory multicore platforms. If your compiler does not support the OpenMP application interface, or if you disable the OpenMP library, the generated code does not split the predictor data and, therefore, processes one observation at a time. To find supported compilers, see Supported Compilers. To disable the OpenMP library, set the`EnableOpenMP`

property of the configuration object to`false`

. For details, see`coder.CodeConfig`

(MATLAB Coder).This table contains notes about the arguments of

`predict`

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

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

`ClassificationKernel`

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.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).

For more information, see Introduction to Code Generation.

## Version History

**Introduced in R2017b**

### R2023a: Generate C/C++ code for prediction

You can generate C/C++ code for the `predict`

function.

## See Also

`ClassificationKernel`

| `fitckernel`

| `resume`

| `rocmetrics`

| `confusionchart`

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