# 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.

Obtain false and true positive rates, and estimate the area under the curve (AUC). Specify that the second class is the positive class.

[fpr,tpr,~,auc] = perfcurve(Y(testInds),posterior(:,2),Mdl.ClassNames(2)); auc

auc = 0.9042

The AUC is close to `1`

, which indicates that the model predicts labels well.

Plot an ROC curve.

figure; plot(fpr,tpr) h = gca; h.XLim(1) = -0.1; h.YLim(2) = 1.1; xlabel('False positive rate') ylabel('True positive rate') title('ROC Curve')

## 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.)

`predict`

classifies observations into the class
yielding the highest score.

`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.

Usage notes and limitations:

`predict`

does not support tall`table`

data.

For more information, see Tall Arrays.

## See Also

`ClassificationKernel`

| `fitckernel`

| `resume`

| `perfcurve`

| `confusionchart`

**Introduced in R2017b**

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