fitrkernel
Fit Gaussian kernel regression model using random feature expansion
Syntax
Description
fitrkernel
trains or crossvalidates a Gaussian kernel
regression model for nonlinear regression. fitrkernel
is more
practical to use for big data applications that have large training sets, but can also
be applied to smaller data sets that fit in memory.
fitrkernel
maps data in a lowdimensional space into a
highdimensional space, then fits a linear model in the highdimensional space by
minimizing the regularized objective function. Obtaining the linear model in the
highdimensional space is equivalent to applying the Gaussian kernel to the model in the
lowdimensional space. Available linear regression models include regularized support
vector machine (SVM) and leastsquares regression models.
To train a nonlinear SVM regression model on inmemory data, see fitrsvm
.
returns a kernel regression model Mdl
= fitrkernel(Tbl
,ResponseVarName
)Mdl
trained using the
predictor variables contained in the table Tbl
and the
response values in Tbl.ResponseVarName
.
specifies options using one or more namevalue pair arguments in addition to any
of the input argument combinations in previous syntaxes. For example, you can
implement leastsquares regression, specify the number of dimension of the
expanded space, or specify crossvalidation options.Mdl
= fitrkernel(___,Name,Value
)
[
also returns the hyperparameter optimization results when you specify
Mdl
,FitInfo
,HyperparameterOptimizationResults
] = fitrkernel(___)OptimizeHyperparameters
.
[
also returns Mdl
,AggregateOptimizationResults
] = fitrkernel(___)AggregateOptimizationResults
, which contains
hyperparameter optimization results when you specify the
OptimizeHyperparameters
and
HyperparameterOptimizationOptions
namevalue arguments.
You must also specify the ConstraintType
and
ConstraintBounds
options of
HyperparameterOptimizationOptions
. You can use this
syntax to optimize on compact model size instead of crossvalidation loss, and
to perform a set of multiple optimization problems that have the same options
but different constraint bounds.
Examples
Train Gaussian Kernel Regression Model
Train a kernel regression model for a tall array by using SVM.
When you perform calculations on tall arrays, MATLAB® uses either a parallel pool (default if you have Parallel Computing Toolbox™) or the local MATLAB session. To run the example using the local MATLAB session when you have Parallel Computing Toolbox, change the global execution environment by using the mapreducer
function.
mapreducer(0)
Create a datastore that references the folder location with the data. The data can be contained in a single file, a collection of files, or an entire folder. Treat 'NA'
values as missing data so that datastore
replaces them with NaN
values. Select a subset of the variables to use. Create a tall table on top of the datastore.
varnames = {'ArrTime','DepTime','ActualElapsedTime'}; ds = datastore('airlinesmall.csv','TreatAsMissing','NA',... 'SelectedVariableNames',varnames); t = tall(ds);
Specify DepTime
and ArrTime
as the predictor variables (X
) and ActualElapsedTime
as the response variable (Y
). Select the observations for which ArrTime
is later than DepTime
.
daytime = t.ArrTime>t.DepTime; Y = t.ActualElapsedTime(daytime); % Response data X = t{daytime,{'DepTime' 'ArrTime'}}; % Predictor data
Standardize the predictor variables.
Z = zscore(X); % Standardize the data
Train a default Gaussian kernel regression model with the standardized predictors. Extract a fit summary to determine how well the optimization algorithm fits the model to the data.
[Mdl,FitInfo] = fitrkernel(Z,Y)
Found 6 chunks. =========================================================================  Solver  Iteration /  Objective  Gradient  Beta relative    Data Pass   magnitude  change  =========================================================================  INIT  0 / 1  4.307833e+01  9.925486e02  NaN   LBFGS  0 / 2  2.782790e+01  7.202403e03  9.891473e01   LBFGS  1 / 3  2.781351e+01  1.806211e02  3.220672e03   LBFGS  2 / 4  2.777773e+01  2.727737e02  9.309939e03   LBFGS  3 / 5  2.768591e+01  2.951422e02  2.833343e02   LBFGS  4 / 6  2.755857e+01  5.124144e02  7.935278e02   LBFGS  5 / 7  2.738896e+01  3.089571e02  4.644920e02   LBFGS  6 / 8  2.716704e+01  2.552696e02  8.596406e02   LBFGS  7 / 9  2.696409e+01  3.088621e02  1.263589e01   LBFGS  8 / 10  2.676203e+01  2.021303e02  1.533927e01   LBFGS  9 / 11  2.660322e+01  1.221361e02  1.351968e01   LBFGS  10 / 12  2.645504e+01  1.486501e02  1.175476e01   LBFGS  11 / 13  2.631323e+01  1.772835e02  1.161909e01   LBFGS  12 / 14  2.625264e+01  5.837906e02  1.422851e01   LBFGS  13 / 15  2.619281e+01  1.294441e02  2.966283e02   LBFGS  14 / 16  2.618220e+01  3.791806e03  9.051274e03   LBFGS  15 / 17  2.617989e+01  3.689255e03  6.364132e03   LBFGS  16 / 18  2.617426e+01  4.200232e03  1.213026e02   LBFGS  17 / 19  2.615914e+01  7.339928e03  2.803348e02   LBFGS  18 / 20  2.620704e+01  2.298098e02  1.749830e01  =========================================================================  Solver  Iteration /  Objective  Gradient  Beta relative    Data Pass   magnitude  change  =========================================================================  LBFGS  18 / 21  2.615554e+01  1.164689e02  8.580878e02   LBFGS  19 / 22  2.614367e+01  3.395507e03  3.938314e02   LBFGS  20 / 23  2.614090e+01  2.349246e03  1.495049e02  ========================================================================
Mdl = RegressionKernel ResponseName: 'Y' Learner: 'svm' NumExpansionDimensions: 64 KernelScale: 1 Lambda: 8.5385e06 BoxConstraint: 1 Epsilon: 5.9303
FitInfo = struct with fields:
Solver: 'LBFGStall'
LossFunction: 'epsiloninsensitive'
Lambda: 8.5385e06
BetaTolerance: 1.0000e03
GradientTolerance: 1.0000e05
ObjectiveValue: 26.1409
GradientMagnitude: 0.0023
RelativeChangeInBeta: 0.0150
FitTime: 17.9573
History: [1x1 struct]
Mdl
is a RegressionKernel
model. To inspect the regression error, you can pass Mdl
and the training data or new data to the loss
function. Or, you can pass Mdl
and new predictor data to the predict
function to predict responses for new observations. You can also pass Mdl
and the training data to the resume
function to continue training.
FitInfo
is a structure array containing optimization information. Use FitInfo
to determine whether optimization termination measurements are satisfactory.
For improved accuracy, you can increase the maximum number of optimization iterations ('IterationLimit'
) and decrease the tolerance values ('BetaTolerance'
and 'GradientTolerance'
) by using the namevalue pair arguments of fitrkernel
. Doing so can improve measures like ObjectiveValue
and RelativeChangeInBeta
in FitInfo
. You can also optimize model parameters by using the 'OptimizeHyperparameters'
namevalue pair argument.
CrossValidate Kernel Regression Model
Load the carbig
data set.
load carbig
Specify the predictor variables (X
) and the response variable (Y
).
X = [Acceleration,Cylinders,Displacement,Horsepower,Weight]; Y = MPG;
Delete rows of X
and Y
where either array has NaN
values. Removing rows with NaN
values before passing data to fitrkernel
can speed up training and reduce memory usage.
R = rmmissing([X Y]); % Data with missing entries removed
X = R(:,1:5);
Y = R(:,end);
Crossvalidate a kernel regression model using 5fold crossvalidation. Standardize the predictor variables.
Mdl = fitrkernel(X,Y,'Kfold',5,'Standardize',true)
Mdl = RegressionPartitionedKernel CrossValidatedModel: 'Kernel' ResponseName: 'Y' NumObservations: 392 KFold: 5 Partition: [1x1 cvpartition] ResponseTransform: 'none'
numel(Mdl.Trained)
ans = 5
Mdl
is a RegressionPartitionedKernel
model. Because fitrkernel
implements fivefold crossvalidation, Mdl
contains five RegressionKernel
models that the software trains on trainingfold (infold) observations.
Examine the crossvalidation loss (mean squared error) for each fold.
kfoldLoss(Mdl,'mode','individual')
ans = 5×1
13.1983
14.2686
23.9162
21.0763
24.3975
Optimize Kernel Regression
Optimize hyperparameters automatically using the OptimizeHyperparameters
namevalue argument.
Load the carbig
data set.
load carbig
Specify the predictor variables (X
) and the response variable (Y
).
X = [Acceleration,Cylinders,Displacement,Horsepower,Weight]; Y = MPG;
Delete rows of X
and Y
where either array has NaN
values. Removing rows with NaN
values before passing data to fitrkernel
can speed up training and reduce memory usage.
R = rmmissing([X Y]); % Data with missing entries removed
X = R(:,1:5);
Y = R(:,end);
Find hyperparameters that minimize fivefold crossvalidation loss by using automatic hyperparameter optimization. Specify OptimizeHyperparameters
as 'auto'
so that fitrkernel
finds the optimal values of the KernelScale
, Lambda
, Epsilon
, and Standardize
namevalue arguments. For reproducibility, set the random seed and use the 'expectedimprovementplus'
acquisition function.
rng('default') [Mdl,FitInfo,HyperparameterOptimizationResults] = fitrkernel(X,Y,'OptimizeHyperparameters','auto',... 'HyperparameterOptimizationOptions',struct('AcquisitionFunctionName','expectedimprovementplus'))
===================================================================================================================================  Iter  Eval  Objective:  Objective  BestSoFar  BestSoFar  KernelScale  Lambda  Epsilon  Standardize    result  log(1+loss)  runtime  (observed)  (estim.)      ===================================================================================================================================  1  Best  4.1521  0.52193  4.1521  4.1521  11.415  0.0017304  615.77  true   2  Best  4.1489  0.11367  4.1489  4.1503  509.07  0.0064454  0.048411  true   3  Accept  5.251  0.84487  4.1489  4.1489  0.0015621  1.8257e05  0.051954  true   4  Accept  4.3329  0.10633  4.1489  4.1489  0.0053278  2.37  17.883  false   5  Accept  4.2414  0.25276  4.1489  4.1489  0.004474  0.13531  14.426  true   6  Best  4.148  0.1422  4.148  4.148  0.43562  2.5339  0.059928  true   7  Accept  4.1521  0.27233  4.148  4.148  3.2193  0.012683  813.56  false   8  Best  3.8438  0.12156  3.8438  3.8439  5.7821  0.065897  2.056  true   9  Accept  4.1305  0.23758  3.8438  3.8439  110.96  0.42454  7.6606  true   10  Best  3.7951  0.34099  3.7951  3.7954  1.1595  0.054292  0.012493  true   11  Accept  4.2311  0.69015  3.7951  3.7954  0.0011423  0.00015862  8.6125  false   12  Best  2.8871  0.86673  2.8871  2.8872  185.22  2.1981e05  1.0401  false   13  Accept  4.1521  0.30883  2.8871  3.0058  993.92  2.6036e06  58.773  false   14  Best  2.8648  0.87323  2.8648  2.8765  196.57  2.2026e05  1.081  false   15  Accept  4.2977  0.20076  2.8648  2.8668  0.017949  1.5685e05  15.01  false   16  Best  2.8016  0.96695  2.8016  2.8017  786  3.4462e06  1.6117  false   17  Accept  2.9032  0.59135  2.8016  2.8026  974.16  0.00019486  1.6661  false   18  Accept  2.9051  1.0062  2.8016  2.8018  288.21  2.6218e06  2.0933  false   19  Accept  3.4438  1.4047  2.8016  2.803  56.999  2.885e06  1.3903  false   20  Accept  2.8436  1.0079  2.8016  2.8032  533.99  2.7293e06  0.6719  false  ===================================================================================================================================  Iter  Eval  Objective:  Objective  BestSoFar  BestSoFar  KernelScale  Lambda  Epsilon  Standardize    result  log(1+loss)  runtime  (observed)  (estim.)      ===================================================================================================================================  21  Accept  2.8301  1.0592  2.8016  2.8024  411.02  3.4347e06  0.98949  false   22  Accept  2.8233  0.50583  2.8016  2.8043  455.25  5.2936e05  1.1189  false   23  Accept  4.1168  0.15522  2.8016  2.802  237.02  0.85493  0.42894  false   24  Best  2.7876  0.8726  2.7876  2.7877  495.51  1.8049e05  1.9006  false   25  Accept  2.8197  0.72568  2.7876  2.7877  927.29  1.128e05  1.1902  false   26  Accept  2.8361  0.72264  2.7876  2.7882  354.44  6.1939e05  2.2591  false   27  Accept  2.7985  0.68054  2.7876  2.7906  506.54  1.4142e05  1.3659  false   28  Accept  2.8163  0.40531  2.7876  2.7905  829.6  1.0965e05  2.7415  false   29  Accept  2.8469  0.7588  2.7876  2.7902  729.48  3.4914e06  0.039087  false   30  Accept  2.882  1.4101  2.7876  2.7902  255.25  3.2869e06  0.059794  false  __________________________________________________________ Optimization completed. MaxObjectiveEvaluations of 30 reached. Total function evaluations: 30 Total elapsed time: 34.514 seconds Total objective function evaluation time: 18.167 Best observed feasible point: KernelScale Lambda Epsilon Standardize ___________ __________ _______ ___________ 495.51 1.8049e05 1.9006 false Observed objective function value = 2.7876 Estimated objective function value = 2.7902 Function evaluation time = 0.8726 Best estimated feasible point (according to models): KernelScale Lambda Epsilon Standardize ___________ __________ _______ ___________ 495.51 1.8049e05 1.9006 false Estimated objective function value = 2.7902 Estimated function evaluation time = 0.67763
Mdl = RegressionKernel ResponseName: 'Y' Learner: 'svm' NumExpansionDimensions: 256 KernelScale: 495.5140 Lambda: 1.8049e05 BoxConstraint: 141.3376 Epsilon: 1.9006
FitInfo = struct with fields:
Solver: 'LBFGSfast'
LossFunction: 'epsiloninsensitive'
Lambda: 1.8049e05
BetaTolerance: 1.0000e04
GradientTolerance: 1.0000e06
ObjectiveValue: 1.3382
GradientMagnitude: 0.0051
RelativeChangeInBeta: 9.4332e05
FitTime: 0.0710
History: []
HyperparameterOptimizationResults = BayesianOptimization with properties: ObjectiveFcn: @createObjFcn/inMemoryObjFcn VariableDescriptions: [6x1 optimizableVariable] Options: [1x1 struct] MinObjective: 2.7876 XAtMinObjective: [1x4 table] MinEstimatedObjective: 2.7902 XAtMinEstimatedObjective: [1x4 table] NumObjectiveEvaluations: 30 TotalElapsedTime: 34.5140 NextPoint: [1x4 table] XTrace: [30x4 table] ObjectiveTrace: [30x1 double] ConstraintsTrace: [] UserDataTrace: {30x1 cell} ObjectiveEvaluationTimeTrace: [30x1 double] IterationTimeTrace: [30x1 double] ErrorTrace: [30x1 double] FeasibilityTrace: [30x1 logical] FeasibilityProbabilityTrace: [30x1 double] IndexOfMinimumTrace: [30x1 double] ObjectiveMinimumTrace: [30x1 double] EstimatedObjectiveMinimumTrace: [30x1 double]
For big data, the optimization procedure can take a long time. If the data set is too large to run the optimization procedure, you can try to optimize the parameters using only partial data. Use the datasample
function and specify 'Replace','false'
to sample data without replacement.
Input Arguments
X
— Predictor data
numeric matrix
Predictor data to which the regression model is fit, specified as an nbyp numeric matrix, where n is the number of observations and p is the number of predictor variables.
The length of Y
and the number of observations in
X
must be equal.
Data Types: single
 double
Tbl
— Sample data
table
Sample data used to train the model, specified as a table. Each row of Tbl
corresponds to one observation, and each column corresponds to one predictor variable.
Optionally, Tbl
can contain one additional column for the response
variable. Multicolumn variables and cell arrays other than cell arrays of character
vectors are not allowed.
If
Tbl
contains the response variable, and you want to use all remaining variables inTbl
as predictors, then specify the response variable by usingResponseVarName
.If
Tbl
contains the response variable, and you want to use only a subset of the remaining variables inTbl
as predictors, then specify a formula by usingformula
.If
Tbl
does not contain the response variable, then specify a response variable by usingY
. The length of the response variable and the number of rows inTbl
must be equal.
ResponseVarName
— Response variable name
name of variable in Tbl
Response variable name, specified as the name of a variable in
Tbl
. The response variable must be a numeric vector.
You must specify ResponseVarName
as a character vector or string
scalar. For example, if Tbl
stores the response variable
Y
as Tbl.Y
, then specify it as
"Y"
. Otherwise, the software treats all columns of
Tbl
, including Y
, as predictors when
training the model.
Data Types: char
 string
formula
— Explanatory model of response variable and subset of predictor variables
character vector  string scalar
Explanatory model of the response variable and a subset of the predictor variables,
specified as a character vector or string scalar in the form
"Y~x1+x2+x3"
. In this form, Y
represents the
response variable, and x1
, x2
, and
x3
represent the predictor variables.
To specify a subset of variables in Tbl
as predictors for
training the model, use a formula. If you specify a formula, then the software does not
use any variables in Tbl
that do not appear in
formula
.
The variable names in the formula must be both variable names in Tbl
(Tbl.Properties.VariableNames
) and valid MATLAB^{®} identifiers. You can verify the variable names in Tbl
by
using the isvarname
function. If the variable names
are not valid, then you can convert them by using the matlab.lang.makeValidName
function.
Data Types: char
 string
Note
The software treats NaN
, empty character vector
(''
), empty string (""
),
<missing>
, and <undefined>
elements as missing values, and removes observations with any of these characteristics:
Missing value in the response variable
At least one missing value in a predictor observation (row in
X
orTbl
)NaN
value or0
weight ('Weights'
)
NameValue Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Namevalue arguments must appear after other arguments, but the order of the
pairs does not matter.
Example: Mdl =
fitrkernel(X,Y,Learner="leastsquares",NumExpansionDimensions=2^15,KernelScale="auto")
implements leastsquares regression after mapping the predictor data to the
2^15
dimensional space using feature expansion with a kernel
scale parameter selected by a heuristic procedure.
Before R2021a, use commas to separate each name and value, and enclose
Name
in quotes.
Example: Mdl =
fitrkernel(X,Y,'Learner','leastsquares','NumExpansionDimensions',2^15,'KernelScale','auto')
Note
You cannot use any crossvalidation namevalue argument together with the
OptimizeHyperparameters
namevalue argument. You can modify the
crossvalidation for OptimizeHyperparameters
only by using the
HyperparameterOptimizationOptions
namevalue argument.
BoxConstraint
— Box constraint
1
(default)  positive scalar
Box
constraint, specified as the commaseparated pair consisting
of 'BoxConstraint'
and a positive scalar.
This argument is valid only when 'Learner'
is
'svm'
(default) and you do not specify a value for
the regularization term strength 'Lambda'
. You can
specify either 'BoxConstraint'
or
'Lambda'
because the box constraint
(C) and the regularization term strength
(λ) are related by C =
1/(λn), where n is the number of
observations (rows in X
).
Example: 'BoxConstraint',100
Data Types: single
 double
Epsilon
— Half width of epsiloninsensitive band
'auto'
(default)  nonnegative scalar value
Half the width of the epsiloninsensitive band, specified as the
commaseparated pair consisting of 'Epsilon'
and
'auto'
or a nonnegative scalar value.
For 'auto'
, the fitrkernel
function determines the value of Epsilon
as
iqr(Y)/13.49
, which is an estimate of a tenth of
the standard deviation using the interquartile range of the response
variable Y
. If iqr(Y)
is equal to
zero, then fitrkernel
sets the value of
Epsilon
to 0.1.
'Epsilon'
is valid only when
Learner
is svm
.
Example: 'Epsilon',0.3
Data Types: single
 double
NumExpansionDimensions
— Number of dimensions of expanded space
'auto'
(default)  positive integer
Number of dimensions of the expanded space, specified as the
commaseparated pair consisting of
'NumExpansionDimensions'
and
'auto'
or a positive integer. For
'auto'
, the fitrkernel
function selects the number of dimensions using
2.^ceil(min(log2(p)+5,15))
, where
p
is the number of predictors.
Example: 'NumExpansionDimensions',2^15
Data Types: char
 string
 single
 double
KernelScale
— Kernel scale parameter
1
(default)  'auto'
 positive scalar
Kernel scale parameter, specified as the commaseparated pair
consisting of 'KernelScale'
and
'auto'
or a positive scalar. MATLAB obtains the random basis for random feature expansion by
using the kernel scale parameter. For details, see Random Feature Expansion.
If you specify 'auto'
, then MATLAB selects an appropriate kernel scale parameter using a
heuristic procedure. This heuristic procedure uses subsampling, so
estimates can vary from one call to another. Therefore, to reproduce
results, set a random number seed by using rng
before
training.
Example: 'KernelScale','auto'
Data Types: char
 string
 single
 double
Lambda
— Regularization term strength
'auto'
(default)  nonnegative scalar
Regularization term strength, specified as the commaseparated pair consisting of 'Lambda'
and 'auto'
or a nonnegative scalar.
For 'auto'
, the value of Lambda
is
1/n, where n is the number of
observations.
When Learner
is 'svm'
, you can specify either
BoxConstraint
or Lambda
because the box
constraint (C) and the regularization term strength
(λ) are related by C =
1/(λn).
Example: 'Lambda',0.01
Data Types: char
 string
 single
 double
Learner
— Linear regression model type
'svm'
(default)  'leastsquares'
Linear regression model type, specified as the commaseparated pair
consisting of 'Learner'
and 'svm'
or 'leastsquares'
.
In the following table, $$f\left(x\right)=T(x)\beta +b.$$
x is an observation (row vector) from p predictor variables.
$$T(\xb7)$$ is a transformation of an observation (row vector) for feature expansion. T(x) maps x in $${\mathbb{R}}^{p}$$ to a highdimensional space ($${\mathbb{R}}^{m}$$).
β is a vector of coefficients.
b is the scalar bias.
Value  Algorithm  Response range  Loss function 

'leastsquares'  Linear regression via ordinary least squares  y ∊ (∞,∞)  Mean squared error (MSE): $$\ell \left[y,f\left(x\right)\right]=\frac{1}{2}{\left[yf\left(x\right)\right]}^{2}$$ 
'svm'  Support vector machine regression  Same as 'leastsquares'  Epsiloninsensitive: $$\ell \left[y,f\left(x\right)\right]=\mathrm{max}\left[0,\leftyf\left(x\right)\right\epsilon \right]$$ 
Example: 'Learner','leastsquares'
Standardize
— Flag to standardize predictor data
false
or 0
(default)  true
or 1
Since R2023b
Flag to standardize the predictor data, specified as a numeric or logical 0
(false
) or 1
(true
). If you
set Standardize
to true
, then the software
centers and scales each numeric predictor variable by the corresponding column mean and
standard deviation. The software does not standardize the categorical predictors.
Example: "Standardize",true
Data Types: single
 double
 logical
Verbose
— Verbosity level
0
(default)  1
Verbosity level, specified as the commaseparated pair consisting of
'Verbose'
and either 0
or
1
. Verbose
controls the amount
of diagnostic information fitrkernel
displays at
the command line.
Value  Description 

0  fitrkernel does not display
diagnostic information. 
1  fitrkernel displays and stores
the value of the objective function, gradient magnitude,
and other diagnostic information.
FitInfo.History contains the
diagnostic information. 
Example: 'Verbose',1
Data Types: single
 double
BlockSize
— Maximum amount of allocated memory
4e^3
(4GB) (default)  positive scalar
Maximum amount of allocated memory (in megabytes), specified as the
commaseparated pair consisting of 'BlockSize'
and a
positive scalar.
If fitrkernel
requires more memory than the value
of BlockSize
to hold the transformed predictor data,
then MATLAB uses a blockwise strategy. For details about the
blockwise strategy, see Algorithms.
Example: 'BlockSize',1e4
Data Types: single
 double
RandomStream
— Random number stream
global stream (default)  random stream object
Random number stream for reproducibility of data transformation,
specified as the commaseparated pair consisting of
'RandomStream'
and a random stream object. For
details, see Random Feature Expansion.
Use 'RandomStream'
to reproduce the random basis
functions that fitrkernel
uses to transform the data
in X
to a highdimensional space. For details, see
Managing the Global Stream Using RandStream and Creating and Controlling a Random Number Stream.
Example: 'RandomStream',RandStream('mlfg6331_64')
CategoricalPredictors
— Categorical predictors list
vector of positive integers  logical vector  character matrix  string array  cell array of character vectors  'all'
Categorical predictors list, specified as one of the values in this table.
Value  Description 

Vector of positive integers 
Each entry in the vector is an index value indicating that the corresponding predictor is
categorical. The index values are between 1 and If 
Logical vector 
A 
Character matrix  Each row of the matrix is the name of a predictor variable. The names must match the entries in PredictorNames . Pad the names with extra blanks so each row of the character matrix has the same length. 
String array or cell array of character vectors  Each element in the array is the name of a predictor variable. The names must match the entries in PredictorNames . 
"all"  All predictors are categorical. 
By default, if the
predictor data is in a table (Tbl
), fitrkernel
assumes that a variable is categorical if it is a logical vector, categorical vector, character
array, string array, or cell array of character vectors. If the predictor data is a matrix
(X
), fitrkernel
assumes that all predictors are
continuous. To identify any other predictors as categorical predictors, specify them by using
the CategoricalPredictors
namevalue argument.
For the identified categorical predictors, fitrkernel
creates dummy variables using two different schemes, depending on whether a categorical variable is unordered or ordered. For an unordered categorical variable, fitrkernel
creates one dummy variable for each level of the categorical variable. For an ordered categorical variable, fitrkernel
creates one less dummy variable than the number of categories. For details, see Automatic Creation of Dummy Variables.
Example: 'CategoricalPredictors','all'
Data Types: single
 double
 logical
 char
 string
 cell
PredictorNames
— Predictor variable names
string array of unique names  cell array of unique character vectors
Predictor variable names, specified as a string array of unique names or cell array of unique
character vectors. The functionality of PredictorNames
depends on the
way you supply the training data.
If you supply
X
andY
, then you can usePredictorNames
to assign names to the predictor variables inX
.The order of the names in
PredictorNames
must correspond to the column order ofX
. That is,PredictorNames{1}
is the name ofX(:,1)
,PredictorNames{2}
is the name ofX(:,2)
, and so on. Also,size(X,2)
andnumel(PredictorNames)
must be equal.By default,
PredictorNames
is{'x1','x2',...}
.
If you supply
Tbl
, then you can usePredictorNames
to choose which predictor variables to use in training. That is,fitrkernel
uses only the predictor variables inPredictorNames
and the response variable during training.PredictorNames
must be a subset ofTbl.Properties.VariableNames
and cannot include the name of the response variable.By default,
PredictorNames
contains the names of all predictor variables.A good practice is to specify the predictors for training using either
PredictorNames
orformula
, but not both.
Example: "PredictorNames",["SepalLength","SepalWidth","PetalLength","PetalWidth"]
Data Types: string
 cell
ResponseName
— Response variable name
"Y"
(default)  character vector  string scalar
Response variable name, specified as a character vector or string scalar.
If you supply
Y
, then you can useResponseName
to specify a name for the response variable.If you supply
ResponseVarName
orformula
, then you cannot useResponseName
.
Example: ResponseName="response"
Data Types: char
 string
ResponseTransform
— Function for transforming raw response values
"none"
(default)  function handle  function name
Function for transforming raw response values, specified as a function handle or
function name. The default is "none"
, which means
@(y)y
, or no transformation. The function should accept a vector
(the original response values) and return a vector of the same size (the transformed
response values).
Example: Suppose you create a function handle that applies an exponential
transformation to an input vector by using myfunction = @(y)exp(y)
.
Then, you can specify the response transformation as
ResponseTransform=myfunction
.
Data Types: char
 string
 function_handle
Weights
— Observation weights
vector of scalar values  name of variable in Tbl
Observation weights, specified as the commaseparated pair consisting
of 'Weights'
and a vector of scalar values or the
name of a variable in Tbl
. The software weights
each observation (or row) in X
or
Tbl
with the corresponding value in
Weights
. The length of
Weights
must equal the number of rows in
X
or Tbl
.
If you specify the input data as a table Tbl
,
then Weights
can be the name of a variable in
Tbl
that contains a numeric vector. In this
case, you must specify Weights
as a character
vector or string scalar. For example, if weights vector
W
is stored as Tbl.W
, then
specify it as 'W'
. Otherwise, the software treats all
columns of Tbl
, including W
, as
predictors when training the model.
By default, Weights
is
ones(n,1)
, where n
is the
number of observations in X
or
Tbl
.
fitrkernel
normalizes the weights to sum to
1.
Data Types: single
 double
 char
 string
CrossVal
— Crossvalidation flag
'off'
(default)  'on'
Crossvalidation flag, specified as the commaseparated pair
consisting of 'Crossval'
and 'on'
or 'off'
.
If you specify 'on'
, then the software implements
10fold crossvalidation.
You can override this crossvalidation setting using the
CVPartition
, Holdout
,
KFold
, or Leaveout
namevalue pair argument. You can use only one crossvalidation
namevalue pair argument at a time to create a crossvalidated
model.
Example: 'Crossval','on'
CVPartition
— Crossvalidation partition
[]
(default)  cvpartition
object
Crossvalidation partition, specified as a cvpartition
object that specifies the type of crossvalidation and the
indexing for the training and validation sets.
To create a crossvalidated model, you can specify only one of these four namevalue
arguments: CVPartition
, Holdout
,
KFold
, or Leaveout
.
Example: Suppose you create a random partition for 5fold crossvalidation on 500
observations by using cvp = cvpartition(500,KFold=5)
. Then, you can
specify the crossvalidation partition by setting
CVPartition=cvp
.
Holdout
— Fraction of data for holdout validation
scalar value in the range (0,1)
Fraction of the data used for holdout validation, specified as a scalar value in the range
(0,1). If you specify Holdout=p
, then the software completes these
steps:
Randomly select and reserve
p*100
% of the data as validation data, and train the model using the rest of the data.Store the compact trained model in the
Trained
property of the crossvalidated model.
To create a crossvalidated model, you can specify only one of these four namevalue
arguments: CVPartition
, Holdout
,
KFold
, or Leaveout
.
Example: Holdout=0.1
Data Types: double
 single
KFold
— Number of folds
10
(default)  positive integer value greater than 1
Number of folds to use in the crossvalidated model, specified as a positive integer value
greater than 1. If you specify KFold=k
, then the software completes
these steps:
Randomly partition the data into
k
sets.For each set, reserve the set as validation data, and train the model using the other
k
– 1 sets.Store the
k
compact trained models in ak
by1 cell vector in theTrained
property of the crossvalidated model.
To create a crossvalidated model, you can specify only one of these four namevalue
arguments: CVPartition
, Holdout
,
KFold
, or Leaveout
.
Example: KFold=5
Data Types: single
 double
Leaveout
— Leaveoneout crossvalidation flag
'off'
(default)  'on'
Leaveoneout crossvalidation flag, specified as the commaseparated pair consisting of
'Leaveout'
and 'on'
or
'off'
. If you specify 'Leaveout','on'
, then,
for each of the n observations (where n is the
number of observations excluding missing observations), the software completes these
steps:
Reserve the observation as validation data, and train the model using the other n – 1 observations.
Store the n compact, trained models in the cells of an nby1 cell vector in the
Trained
property of the crossvalidated model.
To create a crossvalidated model, you can use one of these
four namevalue pair arguments only: CVPartition
, Holdout
, KFold
,
or Leaveout
.
Example: 'Leaveout','on'
BetaTolerance
— Relative tolerance on linear coefficients and bias term
1e4
(default)  nonnegative scalar
Relative tolerance on the linear coefficients and the bias term (intercept), specified as a nonnegative scalar.
Let $${B}_{t}=\left[{\beta}_{t}{}^{\prime}\text{\hspace{0.17em}}\text{\hspace{0.17em}}{b}_{t}\right]$$, that is, the vector of the coefficients and the bias term at optimization iteration t. If $${\Vert \frac{{B}_{t}{B}_{t1}}{{B}_{t}}\Vert}_{2}<\text{BetaTolerance}$$, then optimization terminates.
If you also specify GradientTolerance
, then optimization terminates when the software satisfies either stopping criterion.
Example: 'BetaTolerance',1e6
Data Types: single
 double
GradientTolerance
— Absolute gradient tolerance
1e6
(default)  nonnegative scalar
Absolute gradient tolerance, specified as a nonnegative scalar.
Let $$\nabla {\mathcal{L}}_{t}$$ be the gradient vector of the objective function with respect to the coefficients and bias term at optimization iteration t. If $${\Vert \nabla {\mathcal{L}}_{t}\Vert}_{\infty}=\mathrm{max}\left\nabla {\mathcal{L}}_{t}\right<\text{GradientTolerance}$$, then optimization terminates.
If you also specify BetaTolerance
, then optimization terminates when the
software satisfies either stopping criterion.
Example: 'GradientTolerance',1e5
Data Types: single
 double
HessianHistorySize
— Size of history buffer for Hessian approximation
15
(default)  positive integer
Size of the history buffer for Hessian approximation, specified as the
commaseparated pair consisting of
'HessianHistorySize'
and a positive integer. At
each iteration, fitrkernel
composes the Hessian by
using statistics from the latest HessianHistorySize
iterations.
Example: 'HessianHistorySize',10
Data Types: single
 double
IterationLimit
— Maximum number of optimization iterations
positive integer
Maximum number of optimization iterations, specified as the
commaseparated pair consisting of 'IterationLimit'
and a positive integer.
The default value is 1000 if the transformed data fits in memory, as
specified by BlockSize
. Otherwise, the default
value is 100.
Example: 'IterationLimit',500
Data Types: single
 double
OptimizeHyperparameters
— Parameters to optimize
'none'
(default)  'auto'
 'all'
 string array or cell array of eligible parameter names  vector of optimizableVariable
objects
Parameters to optimize, specified as the commaseparated pair
consisting of 'OptimizeHyperparameters'
and one of
these values:
'none'
— Do not optimize.'auto'
— Use{'KernelScale','Lambda','Epsilon','Standardize'}
.'all'
— Optimize all eligible parameters.Cell array of eligible parameter names.
Vector of
optimizableVariable
objects, typically the output ofhyperparameters
.
The optimization attempts to minimize the crossvalidation loss
(error) for fitrkernel
by varying the parameters. To control the
crossvalidation type and other aspects of the optimization, use the
HyperparameterOptimizationOptions
namevalue argument. When you use
HyperparameterOptimizationOptions
, you can use the (compact) model size
instead of the crossvalidation loss as the optimization objective by setting the
ConstraintType
and ConstraintBounds
options.
Note
The values of OptimizeHyperparameters
override any values you
specify using other namevalue arguments. For example, setting
OptimizeHyperparameters
to "auto"
causes
fitrkernel
to optimize hyperparameters corresponding to the
"auto"
option and to ignore any specified values for the
hyperparameters.
The eligible parameters for fitrkernel
are:
Epsilon
—fitrkernel
searches among positive values, by default logscaled in the range[1e3,1e2]*iqr(Y)/1.349
.KernelScale
—fitrkernel
searches among positive values, by default logscaled in the range[1e3,1e3]
.Lambda
—fitrkernel
searches among positive values, by default logscaled in the range[1e3,1e3]/n
, wheren
is the number of observations.Learner
—fitrkernel
searches among'svm'
and'leastsquares'
.NumExpansionDimensions
—fitrkernel
searches among positive integers, by default logscaled in the range[100,10000]
.Standardize
—fitrkernel
searches amongtrue
andfalse
.
Set nondefault parameters by passing a vector of
optimizableVariable
objects that have nondefault
values. For example:
load carsmall params = hyperparameters('fitrkernel',[Horsepower,Weight],MPG); params(2).Range = [1e4,1e6];
Pass params
as the value of
'OptimizeHyperparameters'
.
By default, the iterative display appears at the command line,
and plots appear according to the number of hyperparameters in the optimization. For the
optimization and plots, the objective function is log(1 + crossvalidation loss). To control the iterative display, set the Verbose
field of
the 'HyperparameterOptimizationOptions'
namevalue argument. To control the
plots, set the ShowPlots
field of the
'HyperparameterOptimizationOptions'
namevalue argument.
For an example, see Optimize Kernel Regression.
Example: 'OptimizeHyperparameters','auto'
HyperparameterOptimizationOptions
— Options for optimization
HyperparameterOptimizationOptions
object  structure
Options for optimization, specified as a HyperparameterOptimizationOptions
object or a structure. This argument
modifies the effect of the OptimizeHyperparameters
namevalue
argument. If you specify HyperparameterOptimizationOptions
, you must
also specify OptimizeHyperparameters
. All the options are optional.
However, you must set ConstraintBounds
and
ConstraintType
to return
AggregateOptimizationResults
. The options that you can set in a
structure are the same as those in the
HyperparameterOptimizationOptions
object.
Option  Values  Default 

Optimizer 
 "bayesopt" 
ConstraintBounds  Constraint bounds for N optimization problems,
specified as an Nby2 numeric matrix or
 [] 
ConstraintTarget  Constraint target for the optimization problems, specified as
 If you specify ConstraintBounds and
ConstraintType , then the default value is
"matlab" . Otherwise, the default value is
[] . 
ConstraintType  Constraint type for the optimization problems, specified as
 [] 
AcquisitionFunctionName  Type of acquisition function:
Acquisition functions whose names include
 "expectedimprovementpersecondplus" 
MaxObjectiveEvaluations  Maximum number of objective function evaluations. If you specify multiple
optimization problems using ConstraintBounds , the value of
MaxObjectiveEvaluations applies to each optimization
problem individually.  30 for "bayesopt" and
"randomsearch" , and the entire grid for
"gridsearch" 
MaxTime  Time limit for the optimization, specified as a nonnegative real
scalar. The time limit is in seconds, as measured by  Inf 
NumGridDivisions  For Optimizer="gridsearch" , the number of values in each
dimension. The value can be a vector of positive integers giving the number of
values for each dimension, or a scalar that applies to all dimensions. This
option is ignored for categorical variables.  10 
ShowPlots  Logical value indicating whether to show plots of the optimization progress.
If this option is true , the software plots the best observed
objective function value against the iteration number. If you use Bayesian
optimization (Optimizer ="bayesopt" ), then
the software also plots the best estimated objective function value. The best
observed objective function values and best estimated objective function values
correspond to the values in the BestSoFar (observed) and
BestSoFar (estim.) columns of the iterative display,
respectively. You can find these values in the properties ObjectiveMinimumTrace and EstimatedObjectiveMinimumTrace of
Mdl.HyperparameterOptimizationResults . If the problem
includes one or two optimization parameters for Bayesian optimization, then
ShowPlots also plots a model of the objective function
against the parameters.  true 
SaveIntermediateResults  Logical value indicating whether to save the optimization results. If this
option is true , the software overwrites a workspace variable
named "BayesoptResults" at each iteration. The variable is a
BayesianOptimization object. If you
specify multiple optimization problems using
ConstraintBounds , the workspace variable is an AggregateBayesianOptimization object named
"AggregateBayesoptResults" .  false 
Verbose  Display level at the command line:
For details, see the  1 
UseParallel  Logical value indicating whether to run the Bayesian optimization in parallel, which requires Parallel Computing Toolbox™. Due to the nonreproducibility of parallel timing, parallel Bayesian optimization does not necessarily yield reproducible results. For details, see Parallel Bayesian Optimization.  false 
Repartition  Logical value indicating whether to repartition the crossvalidation at
every iteration. If this option is A value of
 false 
Specify only one of the following three options.  
CVPartition  cvpartition object created by cvpartition  Kfold=5 if you do not specify a
crossvalidation option 
Holdout  Scalar in the range (0,1) representing the holdout
fraction  
Kfold  Integer greater than 1 
Example: HyperparameterOptimizationOptions=struct(UseParallel=true)
Output Arguments
Mdl
— Trained kernel regression model
RegressionKernel
model object  RegressionPartitionedKernel
crossvalidated model
object
Trained kernel regression model, returned as a RegressionKernel
model object or RegressionPartitionedKernel
crossvalidated model
object.
If you set any of the namevalue pair arguments
CrossVal
, CVPartition
,
Holdout
, KFold
, or
Leaveout
, then Mdl
is a
RegressionPartitionedKernel
crossvalidated model.
Otherwise, Mdl
is a RegressionKernel
model.
To reference properties of Mdl
, use dot notation. For
example, enter Mdl.NumExpansionDimensions
in the Command
Window to display the number of dimensions of the expanded space.
If you specify OptimizeHyperparameters
and
set the ConstraintType
and ConstraintBounds
options of
HyperparameterOptimizationOptions
, then Mdl
is an
Nby1 cell array of model objects, where N is equal
to the number of rows in ConstraintBounds
. If none of the optimization
problems yields a feasible model, then each cell array value is []
.
Note
Unlike other regression models, and for economical memory usage, a
RegressionKernel
model object does not store the
training data or training process details (for example, convergence
history).
AggregateOptimizationResults
— Aggregate optimization results
AggregateBayesianOptimization
object
Aggregate optimization results for multiple optimization problems, returned as an AggregateBayesianOptimization
object. To return
AggregateOptimizationResults
, you must specify
OptimizeHyperparameters
and
HyperparameterOptimizationOptions
. You must also specify the
ConstraintType
and ConstraintBounds
options of HyperparameterOptimizationOptions
. For an example that
shows how to produce this output, see Hyperparameter Optimization with Multiple Constraint Bounds.
FitInfo
— Optimization details
structure array
Optimization details, returned as a structure array including fields described in this table. The fields contain final values or namevalue pair argument specifications.
Field  Description 

Solver  Objective function minimization technique:

LossFunction  Loss function. Either mean squared error (MSE) or
epsiloninsensitive, depending on the type of linear
regression model. See Learner . 
Lambda  Regularization term strength. See
Lambda . 
BetaTolerance  Relative tolerance on the linear coefficients and the
bias term. See BetaTolerance . 
GradientTolerance  Absolute gradient tolerance. See
GradientTolerance . 
ObjectiveValue  Value of the objective function when optimization terminates. The regression loss plus the regularization term compose the objective function. 
GradientMagnitude  Infinite norm of the gradient vector of the objective
function when optimization terminates. See
GradientTolerance . 
RelativeChangeInBeta  Relative changes in the linear coefficients and the bias
term when optimization terminates. See
BetaTolerance . 
FitTime  Elapsed, wallclock time (in seconds) required to fit the model to the data. 
History  History of optimization information. This field also
includes the optimization information from training
Mdl . This field is empty
([] ) if you specify
'Verbose',0 . For details, see
Verbose and Algorithms. 
To access fields, use dot notation. For example, to access the vector of
objective function values for each iteration, enter
FitInfo.ObjectiveValue
in the Command Window.
If you specify
OptimizeHyperparameters
and set the
ConstraintType
and
ConstraintBounds
options of
HyperparameterOptimizationOptions
, then
Fitinfo
is an Nby1 cell array of
structure arrays, where N is equal to the number of rows
in ConstraintBounds
.
Examine the information provided by FitInfo
to assess
whether convergence is satisfactory.
HyperparameterOptimizationResults
— Crossvalidation optimization of hyperparameters
BayesianOptimization
object  AggregateBayesianOptimization
object  table of hyperparameters and associated values
Crossvalidation optimization of hyperparameters, returned as a BayesianOptimization
object, an AggregateBayesianOptimization
object, or a table of hyperparameters and
associated values. The output is nonempty when
OptimizeHyperparameters
has a value other than
"none"
.
If you set the ConstraintType
and
ConstraintBounds
options in
HyperparameterOptimizationOptions
, then
HyperparameterOptimizationResults
is an AggregateBayesianOptimization
object. Otherwise, the value of
HyperparameterOptimizationResults
depends on the value of the
Optimizer
option in
HyperparameterOptimizationOptions
.
Value of Optimizer Option  Value of HyperparameterOptimizationResults 

"bayesopt" (default)  Object of class BayesianOptimization 
"gridsearch" or "randomsearch"  Table of hyperparameters used, observed objective function values (crossvalidation loss), and rank of observations from lowest (best) to highest (worst) 
More About
Random Feature Expansion
Random feature expansion, such as Random Kitchen Sinks [1] or Fastfood [2], is a scheme to approximate Gaussian kernels of the kernel regression algorithm for big data in a computationally efficient way. Random feature expansion is more practical for big data applications that have large training sets, but can also be applied to smaller data sets that fit in memory.
After mapping the predictor data into a highdimensional space, the kernel regression algorithm searches for an optimal function that deviates from each response data point (y_{i}) by values no greater than the epsilon margin (ε).
Some regression problems cannot be described adequately using a linear model. In such cases, obtain a nonlinear regression model by replacing the dot product x_{1}x_{2}′ with a nonlinear kernel function $$G({x}_{1},{x}_{2})=\langle \phi ({x}_{1}),\phi ({x}_{2})\rangle $$, where x_{i} is the ith observation (row vector) and φ(x_{i}) is a transformation that maps x_{i} to a highdimensional space (called the “kernel trick”). However, evaluating G(x_{1},x_{2}), the Gram matrix, for each pair of observations is computationally expensive for a large data set (large n).
The random feature expansion scheme finds a random transformation so that its dot product approximates the Gaussian kernel. That is,
$$G({x}_{1},{x}_{2})=\langle \phi ({x}_{1}),\phi ({x}_{2})\rangle \approx T({x}_{1})T({x}_{2})\text{'},$$
where T(x) maps x in $${\mathbb{R}}^{p}$$ to a highdimensional space ($${\mathbb{R}}^{m}$$). The Random Kitchen Sinks [1] scheme uses the random transformation
$$T(x)={m}^{1/2}\mathrm{exp}\left(iZx\text{'}\right)\text{'},$$
where $$Z\in {\mathbb{R}}^{m\times p}$$ is a sample drawn from $$N\left(0,{\sigma}^{2}\right)$$ and σ is a kernel scale. This scheme requires O(mp) computation and storage. The Fastfood [2] scheme introduces
another random basis V instead of Z using Hadamard
matrices combined with Gaussian scaling matrices. This random basis reduces computation cost
to O(mlog
p) and reduces storage to O(m).
You can specify values for m and σ, using the
NumExpansionDimensions
and KernelScale
namevalue pair arguments of fitrkernel
, respectively.
The fitrkernel
function uses the Fastfood scheme for random feature
expansion and uses linear regression to train a Gaussian kernel regression model. Unlike
solvers in the fitrsvm
function, which require computation of the
nbyn Gram matrix, the solver in
fitrkernel
only needs to form a matrix of size
nbym, with m typically much
less than n for big data.
Box Constraint
A box constraint is a parameter that controls the maximum penalty imposed on observations that lie outside the epsilon margin (ε), and helps to prevent overfitting (regularization). Increasing the box constraint can lead to longer training times.
The box constraint (C) and the regularization term strength (λ) are related by C = 1/(λn), where n is the number of observations.
Tips
Standardizing predictors before training a model can be helpful.
You can standardize training data and scale test data to have the same scale as the training data by using the
normalize
function.Alternatively, use the
Standardize
namevalue argument to standardize the numeric predictors before training. The returned model includes the predictor means and standard deviations in itsMu
andSigma
properties, respectively. (since R2023b)
After training a model, you can generate C/C++ code that predicts responses for new data. Generating C/C++ code requires MATLAB Coder™. For details, see Introduction to Code Generation.
Algorithms
fitrkernel
minimizes the regularized objective function using a Limitedmemory BroydenFletcherGoldfarbShanno (LBFGS) solver with ridge (L_{2}) regularization. To find the type of LBFGS solver used for training, type FitInfo.Solver
in the Command Window.
'LBFGSfast'
— LBFGS solver.'LBFGSblockwise'
— LBFGS solver with a blockwise strategy. Iffitrkernel
requires more memory than the value ofBlockSize
to hold the transformed predictor data, then the function uses a blockwise strategy.'LBFGStall'
— LBFGS solver with a blockwise strategy for tall arrays.
When fitrkernel
uses a blockwise strategy, it implements LBFGS by
distributing the calculation of the loss and gradient among different parts of the data at
each iteration. Also, fitrkernel
refines the initial estimates of the
linear coefficients and the bias term by fitting the model locally to parts of the data and
combining the coefficients by averaging. If you specify 'Verbose',1
, then
fitrkernel
displays diagnostic information for each data pass and
stores the information in the History
field of
FitInfo
.
When fitrkernel
does not use a blockwise strategy, the initial estimates are zeros. If you specify 'Verbose',1
, then fitrkernel
displays diagnostic information for each iteration and stores the information in the History
field of FitInfo
.
References
[1] Rahimi, A., and B. Recht. “Random Features for LargeScale Kernel Machines.” Advances in Neural Information Processing Systems. Vol. 20, 2008, pp. 1177–1184.
[2] Le, Q., T. Sarlós, and A. Smola. “Fastfood — Approximating Kernel Expansions in Loglinear Time.” Proceedings of the 30th International Conference on Machine Learning. Vol. 28, No. 3, 2013, pp. 244–252.
[3] Huang, P. S., H. Avron, T. N. Sainath, V. Sindhwani, and B. Ramabhadran. “Kernel methods match Deep Neural Networks on TIMIT.” 2014 IEEE International Conference on Acoustics, Speech and Signal Processing. 2014, pp. 205–209.
Extended Capabilities
Tall Arrays
Calculate with arrays that have more rows than fit in memory.
The
fitrkernel
function supports tall arrays with the following usage
notes and limitations:
fitrkernel
does not support talltable
data.Some namevalue pair arguments have different defaults compared to the default values for the inmemory
fitrkernel
function. Supported namevalue pair arguments, and any differences, are:'BoxConstraint'
'Epsilon'
'NumExpansionDimensions'
'KernelScale'
'Lambda'
'Learner'
'Verbose'
— Default value is1
.'BlockSize'
'RandomStream'
'ResponseTransform'
'Weights'
— Value must be a tall array.'BetaTolerance'
— Default value is relaxed to1e–3
.'GradientTolerance'
— Default value is relaxed to1e–5
.'HessianHistorySize'
'IterationLimit'
— Default value is relaxed to20
.'OptimizeHyperparameters'
'HyperparameterOptimizationOptions'
— For crossvalidation, tall optimization supports only'Holdout'
validation. By default, the software selects and reserves 20% of the data as holdout validation data, and trains the model using the rest of the data. You can specify a different value for the holdout fraction by using this argument. For example, specify'HyperparameterOptimizationOptions',struct('Holdout',0.3)
to reserve 30% of the data as validation data.
If
'KernelScale'
is'auto'
, thenfitrkernel
uses the random stream controlled bytallrng
for subsampling. For reproducibility, you must set a random number seed for both the global stream and the random stream controlled bytallrng
.If
'Lambda'
is'auto'
, thenfitrkernel
might take an extra pass through the data to calculate the number of observations inX
.fitrkernel
uses a blockwise strategy. For details, see Algorithms.
For more information, see Tall Arrays.
Automatic Parallel Support
Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.
To perform parallel hyperparameter optimization, use the UseParallel=true
option in the HyperparameterOptimizationOptions
namevalue argument in
the call to the fitrkernel
function.
For more information on parallel hyperparameter optimization, see Parallel Bayesian Optimization.
For general information about parallel computing, see Run MATLAB Functions with Automatic Parallel Support (Parallel Computing Toolbox).
Version History
Introduced in R2018aR2023b: Kernel models support standardization of predictors
Starting in R2023b, fitrkernel
supports the standardization
of numeric predictors. That is, you can specify the Standardize
value as true
to center and scale each numeric predictor variable
by the corresponding column mean and standard deviation. The software does not
standardize the categorical predictors.
You can also optimize the Standardize
hyperparameter by using
the OptimizeHyperparameters
namevalue argument. Unlike in
previous releases, when you specify "auto"
as the
OptimizeHyperparameters
value,
fitrkernel
includes Standardize
as an
optimizable hyperparameter.
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