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
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
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 usingfit
orupdateMetricsAndFit
.
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 inMdl.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 withNaN
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
)
into class j
.
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
where
is a transformation of an observation for feature expansion.
β0 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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