# predict

Predict responses for new observations from kernel incremental learning model

*Since R2022a*

## Description

## Examples

### Predict Responses

Create an incremental learning model by converting a traditionally trained kernel model, and predict responses using both models.

Load the 2015 NYC housing data set. For more details on the data, see NYC Open Data.

`load NYCHousing2015`

Extract the response variable `SALEPRICE`

from the table. For numerical stability, scale `SALEPRICE`

by `1e6`

.

Y = NYCHousing2015.SALEPRICE/1e6; NYCHousing2015.SALEPRICE = [];

To reduce computational cost for this example, remove the `NEIGHBORHOOD`

column, which contains a categorical variable with 254 categories.

NYCHousing2015.NEIGHBORHOOD = [];

Create dummy variable matrices from the other categorical predictors.

catvars = ["BOROUGH","BUILDINGCLASSCATEGORY"]; dumvarstbl = varfun(@(x)dummyvar(categorical(x)),NYCHousing2015, ... InputVariables=catvars); dumvarmat = table2array(dumvarstbl); NYCHousing2015(:,catvars) = [];

Treat all other numeric variables in the table as predictors of sales price. Concatenate the matrix of dummy variables to the rest of the predictor data.

```
idxnum = varfun(@isnumeric,NYCHousing2015,OutputFormat="uniform");
X = [dumvarmat NYCHousing2015{:,idxnum}];
```

Fit a kernel regression model to the entire data set.

Mdl = fitrkernel(X,Y)

Mdl = RegressionKernel ResponseName: 'Y' Learner: 'svm' NumExpansionDimensions: 2048 KernelScale: 1 Lambda: 1.0935e-05 BoxConstraint: 1 Epsilon: 0.0549 Properties, Methods

`Mdl`

is a `RegressionKernel`

model object representing a traditionally trained kernel regression model.

Convert the traditionally trained kernel regression model to a model for incremental learning.

IncrementalMdl = incrementalLearner(Mdl)

IncrementalMdl = incrementalRegressionKernel IsWarm: 1 Metrics: [1x2 table] ResponseTransform: 'none' NumExpansionDimensions: 2048 KernelScale: 1 Properties, Methods

`IncrementalMdl`

is an `incrementalRegressionKernel`

model object prepared for incremental learning.

The `incrementalLearner`

function initializes the incremental learner by passing model parameters to it, along with other information `Mdl`

extracted from the training data. `IncrementalMdl`

is warm (`IsWarm`

is `1`

), which means that incremental learning functions can start tracking performance metrics.

An incremental learner created from converting a traditionally trained model can generate predictions without further processing.

Predict sales prices for all observations using both models.

ttyfit = predict(Mdl,X); ilyfit = predict(IncrementalMdl,X); compareyfit = norm(ttyfit - ilyfit)

compareyfit = 0

The difference between the fitted values generated by the models is `0`

.

### Compute Posterior Class Probabilities

To compute posterior class probabilities, specify a logistic regression incremental learner.

Load the human activity data set. Randomly shuffle the data.

load humanactivity n = numel(actid); rng(10) % For reproducibility idx = randsample(n,n); X = feat(idx,:); Y = actid(idx);

For details on the data set, enter `Description`

at the command line.

Responses can be one of five classes: Sitting, Standing, Walking, Running, or Dancing. Dichotomize the response by identifying whether the subject is moving (`actid`

> 2).

Y = Y > 2;

Create an incremental logistic regression model for binary classification. Prepare it for `predict`

by fitting the model to the first 10 observations.

```
Mdl = incrementalClassificationKernel(Learner="logistic");
initobs = 10;
Mdl = fit(Mdl,X(1:initobs,:),Y(1:initobs));
```

`Mdl`

is an `incrementalClassificationKernel`

model. All its properties are read-only.

Simulate a data stream, and perform the following actions on each incoming chunk of 50 observations:

Call

`predict`

to predict classification scores for the observations in the incoming chunk of data. The classification scores are posterior class probabilities for logistic regression learners.Call

`rocmetrics`

to compute the area under the ROC curve (AUC) using the classification scores, and store the result.Call

`fit`

to fit the model to the incoming chunk. Overwrite the previous incremental model with a new one fitted to the incoming observations.

numObsPerChunk = 50; nchunk = floor((n - initobs)/numObsPerChunk); auc = zeros(nchunk,1); % Incremental learning for j = 1:nchunk ibegin = min(n,numObsPerChunk*(j-1) + 1 + initobs); iend = min(n,numObsPerChunk*j + initobs); idx = ibegin:iend; [~,posteriorProb] = predict(Mdl,X(idx,:)); mdlROC = rocmetrics(Y(idx),posteriorProb,Mdl.ClassNames); auc(j) = mdlROC.AUC(2); Mdl = fit(Mdl,X(idx,:),Y(idx)); end

`Mdl`

is an `incrementalClassificationKernel`

model object trained on all the data in the stream.

Plot the AUC for the incoming chunks of data.

plot(auc) xlim([0 nchunk]) ylabel("AUC") xlabel("Iteration")

The plot suggests that the classifier predicts moving subjects well during incremental learning.

## Input Arguments

`Mdl`

— Incremental learning model

`incrementalClassificationKernel`

model object | `incrementalRegressionKernel`

model object

Incremental learning model, specified as an `incrementalClassificationKernel`

or `incrementalRegressionKernel`

model object. You can create `Mdl`

directly or by converting a supported, traditionally trained machine learning model using the `incrementalLearner`

function. For more details, see the corresponding reference page.

You must configure `Mdl`

to predict labels for a batch of observations.

If

`Mdl`

is a converted, traditionally trained model, you can predict labels without any modifications.Otherwise, you must fit

`Mdl`

to data using`fit`

or`updateMetricsAndFit`

.

`X`

— Batch of predictor data

floating-point matrix

Batch of predictor data, specified as a floating-point matrix of
*n* observations and `Mdl.NumPredictors`

predictor
variables.

**Note**

`predict`

supports only floating-point
input predictor data. If your input data includes categorical data, you must prepare an encoded
version of the categorical data. Use `dummyvar`

to convert each categorical variable
to a numeric matrix of dummy variables. Then, concatenate all dummy variable matrices and any
other numeric predictors. For more details, see Dummy Variables.

**Data Types: **`single`

| `double`

## Output Arguments

`label`

— Predicted responses (labels)

categorical array | character array | string vector | logical vector | cell array of character vectors | floating-point vector

Predicted responses (labels), returned as a categorical or character array;
floating-point, logical, or string vector; or cell array of character vectors with
*n* rows. *n* is the number of observations in
`X`

, and `label(`

is the predicted response for observation
* j*)

`j`

.For regression problems,

`label`

is a floating-point vector.For classification problems,

`label`

has the same data type as the class names stored in`Mdl.ClassNames`

. (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

floating-point matrix

Classification scores, returned as an *n*-by-2 floating-point
matrix when `Mdl`

is an
`incrementalClassificationKernel`

model. *n* is the
number of observations in `X`

.
`score(`

is the score for classifying observation * j*,

*)*

`k`

`j`

into class `k`

.
`Mdl.ClassNames`

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

is `'svm'`

,
`predict`

returns raw classification scores. If
`Mdl.Learner`

is `'logistic'`

, classification scores
are posterior probabilities.

## More About

### Classification Score

For kernel incremental learning models for binary classification, the
raw *classification score* for classifying the observation
*x*, a row vector, into the positive class (second class in
`Mdl.ClassNames`

) is

$$f\left(x\right)={\beta}_{0}+T(x)\beta ,$$

where

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

*β*_{0}is the scalar bias.*β*is the column vector of coefficients.

The raw classification score for classifying *x* into the negative
class (first class in `Mdl.ClassNames`

) is
–*f*(*x*). The software classifies observations into the
class that yields the 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.

## Version History

**Introduced in R2022a**

## See Also

### Objects

### Functions

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