fitckernel
Fit binary Gaussian kernel classifier using random feature expansion
Syntax
Description
fitckernel
trains or crossvalidates a binary Gaussian
kernel classification model for nonlinear classification.
fitckernel
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.
fitckernel
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 classification models include regularized
support vector machine (SVM) and logistic regression models.
To train a nonlinear SVM model for binary classification of inmemory data, see
fitcsvm
.
returns a binary Gaussian kernel classification model trained using the
predictor data in Mdl
= fitckernel(X
,Y
)X
and the corresponding class labels in
Y
. The fitckernel
function maps
the predictors in a lowdimensional space into a highdimensional space, then
fits a binary SVM model to the transformed predictors and class labels. This
linear model is equivalent to the Gaussian kernel classification model in the
lowdimensional space.
returns a kernel classification model Mdl
= fitckernel(Tbl
,ResponseVarName
)Mdl
trained using the
predictor variables contained in the table Tbl
and the
class labels 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 logistic regression, specify the number of dimensions of the expanded
space, or specify to crossvalidate.Mdl
= fitckernel(___,Name,Value
)
[
also returns the hyperparameter optimization results
Mdl
,FitInfo
,HyperparameterOptimizationResults
] = fitckernel(___)HyperparameterOptimizationResults
when you optimize
hyperparameters by using the 'OptimizeHyperparameters'
namevalue pair argument.
Examples
Train Kernel Classification Model
Train a binary kernel classification model using SVM.
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
[n,p] = size(X)
n = 351
p = 34
resp = unique(Y)
resp = 2x1 cell
{'b'}
{'g'}
Train a binary kernel classification model that identifies whether the radar return is bad ('b'
) or good ('g'
). Extract a fit summary to determine how well the optimization algorithm fits the model to the data.
rng('default') % For reproducibility [Mdl,FitInfo] = fitckernel(X,Y)
Mdl = ClassificationKernel ResponseName: 'Y' ClassNames: {'b' 'g'} Learner: 'svm' NumExpansionDimensions: 2048 KernelScale: 1 Lambda: 0.0028 BoxConstraint: 1
FitInfo = struct with fields:
Solver: 'LBFGSfast'
LossFunction: 'hinge'
Lambda: 0.0028
BetaTolerance: 1.0000e04
GradientTolerance: 1.0000e06
ObjectiveValue: 0.2604
GradientMagnitude: 0.0028
RelativeChangeInBeta: 8.2512e05
FitTime: 0.2585
History: []
Mdl
is a ClassificationKernel
model. To inspect the insample classification 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 class labels 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 better 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. 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 Classification Model
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 rng('default') % For reproducibility
Crossvalidate a binary kernel classification model. By default, the software uses 10fold crossvalidation.
CVMdl = fitckernel(X,Y,'CrossVal','on')
CVMdl = ClassificationPartitionedKernel CrossValidatedModel: 'Kernel' ResponseName: 'Y' NumObservations: 351 KFold: 10 Partition: [1x1 cvpartition] ClassNames: {'b' 'g'} ScoreTransform: 'none'
numel(CVMdl.Trained)
ans = 10
CVMdl
is a ClassificationPartitionedKernel
model. Because fitckernel
implements 10fold crossvalidation, CVMdl
contains 10 ClassificationKernel
models that the software trains on trainingfold (infold) observations.
Estimate the crossvalidated classification error.
kfoldLoss(CVMdl)
ans = 0.0940
The classification error rate is approximately 9%.
Optimize Kernel Classifier
Optimize hyperparameters automatically using the OptimizeHyperparameters
namevalue argument.
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
Find hyperparameters that minimize fivefold crossvalidation loss by using automatic hyperparameter optimization. Specify OptimizeHyperparameters
as 'auto'
so that fitckernel
finds optimal values of the KernelScale
, Lambda
, and Standardize
namevalue arguments. For reproducibility, set the random seed and use the 'expectedimprovementplus'
acquisition function.
rng('default') [Mdl,FitInfo,HyperparameterOptimizationResults] = fitckernel(X,Y,'OptimizeHyperparameters','auto',... 'HyperparameterOptimizationOptions',struct('AcquisitionFunctionName','expectedimprovementplus'))
====================================================================================================================  Iter  Eval  Objective  Objective  BestSoFar  BestSoFar  KernelScale  Lambda  Standardize    result   runtime  (observed)  (estim.)     ====================================================================================================================  1  Best  0.35897  1.2179  0.35897  0.35897  3.8653  2.7394  true   2  Accept  0.35897  0.65314  0.35897  0.35897  429.99  0.0006775  false   3  Accept  0.35897  1.7977  0.35897  0.35897  0.11801  0.025493  false   4  Accept  0.41311  1.6759  0.35897  0.35898  0.0010694  9.1346e06  true   5  Accept  0.4245  2.3254  0.35897  0.35898  0.0093918  2.8526e06  false   6  Best  0.17094  1.2406  0.17094  0.17102  15.285  0.0038931  false   7  Accept  0.18234  2.611  0.17094  0.17099  9.9078  0.0090818  false   8  Accept  0.35897  1.9334  0.17094  0.17097  26.961  0.46727  false   9  Best  0.082621  1.275  0.082621  0.082677  7.7184  0.0025676  false   10  Best  0.059829  1.3084  0.059829  0.059839  5.6125  0.0013416  false   11  Accept  0.062678  1.9155  0.059829  0.059793  7.3294  0.00062394  false   12  Best  0.048433  1.5794  0.048433  0.050198  3.7772  0.00032964  false   13  Accept  0.051282  1.067  0.048433  0.049662  3.4417  0.00077524  false   14  Accept  0.054131  1.5461  0.048433  0.051494  4.3694  0.00055199  false   15  Accept  0.051282  1.6725  0.048433  0.04872  1.7463  0.00012886  false   16  Accept  0.048433  1.5304  0.048433  0.048475  3.9086  3.1147e05  false   17  Accept  0.054131  0.95747  0.048433  0.050325  3.1489  9.1315e05  false   18  Accept  0.051282  1.0547  0.048433  0.049131  2.3414  4.8238e06  false   19  Accept  0.065527  1.675  0.048433  0.049103  7.2203  3.2694e06  false   20  Accept  0.054131  1.2915  0.048433  0.051219  3.5381  1.0341e05  false  ====================================================================================================================  Iter  Eval  Objective  Objective  BestSoFar  BestSoFar  KernelScale  Lambda  Standardize    result   runtime  (observed)  (estim.)     ====================================================================================================================  21  Accept  0.068376  1.2292  0.048433  0.051113  1.4267  1.7614e05  false   22  Accept  0.054131  1.4658  0.048433  0.051306  3.2173  2.9573e06  false   23  Accept  0.05698  1.2045  0.048433  0.051195  2.4241  0.0003272  false   24  Accept  0.059829  2.3212  0.048433  0.051098  2.5948  4.5059e05  false   25  Accept  0.059829  1.7581  0.048433  0.05106  7.186  4.1878e05  false   26  Accept  0.062678  1.6112  0.048433  0.050633  3.9212  7.4981e06  false   27  Accept  0.062678  1.1994  0.048433  0.052232  3.9385  0.0002172  false   28  Accept  0.062678  0.82969  0.048433  0.052242  1.4533  2.8533e06  false   29  Best  0.045584  0.93055  0.045584  0.049361  3.1744  0.00082799  false   30  Accept  0.048433  1.266  0.045584  0.048732  2.6844  0.0010257  false  __________________________________________________________ Optimization completed. MaxObjectiveEvaluations of 30 reached. Total function evaluations: 30 Total elapsed time: 60.3599 seconds Total objective function evaluation time: 44.144 Best observed feasible point: KernelScale Lambda Standardize ___________ __________ ___________ 3.1744 0.00082799 false Observed objective function value = 0.045584 Estimated objective function value = 0.048562 Function evaluation time = 0.93055 Best estimated feasible point (according to models): KernelScale Lambda Standardize ___________ __________ ___________ 3.4417 0.00077524 false Estimated objective function value = 0.048732 Estimated function evaluation time = 1.4054
Mdl = ClassificationKernel ResponseName: 'Y' ClassNames: {'b' 'g'} Learner: 'svm' NumExpansionDimensions: 2048 KernelScale: 3.4417 Lambda: 7.7524e04 BoxConstraint: 3.6750
FitInfo = struct with fields:
Solver: 'LBFGSfast'
LossFunction: 'hinge'
Lambda: 7.7524e04
BetaTolerance: 1.0000e04
GradientTolerance: 1.0000e06
ObjectiveValue: 0.1050
GradientMagnitude: 0.0142
RelativeChangeInBeta: 2.8659e05
FitTime: 0.2169
History: []
HyperparameterOptimizationResults = BayesianOptimization with properties: ObjectiveFcn: @createObjFcn/inMemoryObjFcn VariableDescriptions: [5x1 optimizableVariable] Options: [1x1 struct] MinObjective: 0.0456 XAtMinObjective: [1x3 table] MinEstimatedObjective: 0.0487 XAtMinEstimatedObjective: [1x3 table] NumObjectiveEvaluations: 30 TotalElapsedTime: 60.3599 NextPoint: [1x3 table] XTrace: [30x3 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, specified as an nbyp numeric matrix, where n is the number of observations and p is the number of predictors.
The length of Y
and the number of observations in
X
must be equal.
Data Types: single
 double
Y
— Class labels
categorical array  character array  string array  logical vector  numeric vector  cell array of character vectors
Class labels, specified as a categorical, character, or string array, logical or numeric vector, or cell array of character vectors.
fitckernel
supports only binary classification. EitherY
must contain exactly two distinct classes, or you must specify two classes for training by using theClassNames
namevalue pair argument. For multiclass learning, seefitcecoc
.The length of
Y
must be equal to the number of observations inX
orTbl
.If
Y
is a character array, then each label must correspond to one row of the array.A good practice is to specify the class order by using the
ClassNames
namevalue pair argument.
Data Types: categorical
 char
 string
 logical
 single
 double
 cell
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. Multicolumn variables and cell arrays other than cell arrays of character vectors are not allowed.
Optionally, Tbl
can contain a column for the response variable and a column for the observation weights.
The response variable must be a categorical, character, or string array, a logical or numeric vector, or a cell array of character vectors.
fitckernel
supports only binary classification. Either the response variable must contain exactly two distinct classes, or you must specify two classes for training by using theClassNames
namevalue argument. For multiclass learning, seefitcecoc
.A good practice is to specify the order of the classes in the response variable by using the
ClassNames
namevalue argument.
The column for the weights must be a numeric vector.
You must specify the response variable in
Tbl
by usingResponseVarName
orformula
and specify the observation weights inTbl
by usingWeights
.Specify the response variable by using
ResponseVarName
—fitckernel
uses the remaining variables as predictors. To use a subset of the remaining variables inTbl
as predictors, specify predictor variables by usingPredictorNames
.Define a model specification by using
formula
—fitckernel
uses a subset of the variables inTbl
as predictor variables and the response variable, as specified informula
.
If Tbl
does not contain the response variable, then specify a response variable by using Y
. The length of the response variable Y
and the number of rows in Tbl
must be equal. To use a subset of the variables in Tbl
as predictors, specify predictor variables by using PredictorNames
.
Data Types: table
ResponseVarName
— Response variable name
name of variable in Tbl
Response variable name, specified as the name of a variable in
Tbl
.
You must specify ResponseVarName
as a character vector or string scalar.
For example, if the response variable Y
is
stored as Tbl.Y
, then specify it as
"Y"
. Otherwise, the software
treats all columns of Tbl
, including
Y
, as predictors when training
the model.
The response variable must be a categorical, character, or string array; a logical or numeric
vector; or a cell array of character vectors. If
Y
is a character array, then each
element of the response variable must correspond to one row of
the array.
A good practice is to specify the order of the classes by using the
ClassNames
namevalue
argument.
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.
Before R2021a, use commas to separate each name and value, and enclose
Name
in quotes.
Example: Mdl =
fitckernel(X,Y,'Learner','logistic','NumExpansionDimensions',2^15,'KernelScale','auto')
implements logistic 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.
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.
Learner
— Linear classification model type
'svm'
(default)  'logistic'
Linear classification model type, specified as the commaseparated pair consisting of 'Learner'
and 'svm'
or 'logistic'
.
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 

'svm'  Support vector machine  y ∊ {–1,1}; 1 for the positive class and –1 otherwise  Hinge: $$\ell \left[y,f\left(x\right)\right]=\mathrm{max}\left[0,1yf\left(x\right)\right]$$ 
'logistic'  Logistic regression  Same as 'svm'  Deviance (logistic): $$\ell \left[y,f\left(x\right)\right]=\mathrm{log}\left\{1+\mathrm{exp}\left[yf\left(x\right)\right]\right\}$$ 
Example: 'Learner','logistic'
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 fitckernel
function selects the number of dimensions using
2.^ceil(min(log2(p)+5,15))
, where
p
is the number of predictors.
For details, see Random Feature Expansion.
Example: 'NumExpansionDimensions',2^15
Data Types: char
 string
 single
 double
KernelScale
— Kernel scale parameter
1
(default)  "auto"
 positive scalar
Kernel scale parameter, specified as "auto"
or a positive scalar. The
software obtains a random basis for random feature expansion by using the kernel scale
parameter. For details, see Random Feature Expansion.
If you specify "auto"
, then the software 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
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.
Example: 'BoxConstraint',100
Data Types: 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
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
CrossVal
— Flag to train crossvalidated classifier
'off'
(default)  'on'
Flag to train a crossvalidated classifier, specified as the
commaseparated pair consisting of 'Crossval'
and
'on'
or 'off'
.
If you specify 'on'
, then the software trains a
crossvalidated classifier with 10 folds.
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
1e–4
(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=1e–6
Data Types: single
 double
GradientTolerance
— Absolute gradient tolerance
1e–6
(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=1e–5
Data Types: single
 double
IterationLimit
— Maximum number of optimization iterations
positive integer
Maximum number of optimization iterations, specified as a positive integer.
The default value is 1000 if the transformed data fits in memory, as specified by the
BlockSize
namevalue argument. Otherwise, the default value is
100.
Example: IterationLimit=500
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 fitckernel
requires more memory than the value of
'BlockSize'
to hold the transformed predictor data, then the
software 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 a random stream object. For details, see Random Feature Expansion.
Use RandomStream
to reproduce the random basis functions used by
fitckernel
to transform the predictor data 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")
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, fitckernel
composes the Hessian approximation by using
statistics from the latest HessianHistorySize
iterations.
Example: 'HessianHistorySize',10
Data Types: single
 double
Verbose
— Verbosity level
0
(default)  1
Verbosity level, specified as the commaseparated pair consisting of
'Verbose'
and either 0
or
1
. Verbose
controls the
display of diagnostic information at the command line.
Value  Description 

0  fitckernel does not display
diagnostic information. 
1  fitckernel 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
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
), fitckernel
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
), fitckernel
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, fitckernel
creates dummy variables using two different schemes, depending on whether a categorical variable is unordered or ordered. For an unordered categorical variable, fitckernel
creates one dummy variable for each level of the categorical variable. For an ordered categorical variable, fitckernel
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
ClassNames
— Names of classes to use for training
categorical array  character array  string array  logical vector  numeric vector  cell array of character vectors
Names of classes to use for training, specified as a categorical, character, or string
array; a logical or numeric vector; or a cell array of character vectors.
ClassNames
must have the same data type as the response variable
in Tbl
or Y
.
If ClassNames
is a character array, then each element must correspond to one row of the array.
Use ClassNames
to:
Specify the order of the classes during training.
Specify the order of any input or output argument dimension that corresponds to the class order. For example, use
ClassNames
to specify the order of the dimensions ofCost
or the column order of classification scores returned bypredict
.Select a subset of classes for training. For example, suppose that the set of all distinct class names in
Y
is["a","b","c"]
. To train the model using observations from classes"a"
and"c"
only, specify"ClassNames",["a","c"]
.
The default value for ClassNames
is the set of all distinct class names in the response variable in Tbl
or Y
.
Example: "ClassNames",["b","g"]
Data Types: categorical
 char
 string
 logical
 single
 double
 cell
Cost
— Misclassification cost
square matrix  structure array
Misclassification cost, specified as the commaseparated pair consisting of
'Cost'
and a square matrix or structure.
If you specify the square matrix
cost
('Cost',cost
), thencost(i,j)
is the cost of classifying a point into classj
if its true class isi
. That is, the rows correspond to the true class, and the columns correspond to the predicted class. To specify the class order for the corresponding rows and columns ofcost
, use theClassNames
namevalue pair argument.If you specify the structure
S
('Cost',S
), then it must have two fields:S.ClassNames
, which contains the class names as a variable of the same data type asY
S.ClassificationCosts
, which contains the cost matrix with rows and columns ordered as inS.ClassNames
The default value for Cost
is
ones(
, where K
) –
eye(K
)K
is
the number of distinct classes.
fitckernel
uses Cost
to adjust the prior
class probabilities specified in Prior
. Then,
fitckernel
uses the adjusted prior probabilities for
training.
Example: 'Cost',[0 2; 1 0]
Data Types: single
 double
 struct
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,fitckernel
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
Prior
— Prior probabilities
'empirical'
(default)  'uniform'
 numeric vector  structure array
Prior probabilities for each class, specified as the commaseparated pair consisting
of 'Prior'
and 'empirical'
,
'uniform'
, a numeric vector, or a structure array.
This table summarizes the available options for setting prior probabilities.
Value  Description 

'empirical'  The class prior probabilities are the class relative frequencies
in Y . 
'uniform'  All class prior probabilities are equal to
1/K , where
K is the number of classes. 
numeric vector  Each element is a class prior probability. Order the elements
according to their order in Y . If you specify
the order using the 'ClassNames' namevalue
pair argument, then order the elements accordingly. 
structure array 
A structure

fitckernel
normalizes the prior probabilities in
Prior
to sum to 1.
Example: 'Prior',struct('ClassNames',{{'setosa','versicolor'}},'ClassProbs',1:2)
Data Types: char
 string
 double
 single
 struct
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
ScoreTransform
— Score transformation
"none"
(default)  "doublelogit"
 "invlogit"
 "ismax"
 "logit"
 function handle  ...
Score transformation, specified as a character vector, string scalar, or function handle.
This table summarizes the available character vectors and string scalars.
Value  Description 

"doublelogit"  1/(1 + e^{–2x}) 
"invlogit"  log(x / (1 – x)) 
"ismax"  Sets the score for the class with the largest score to 1, and sets the scores for all other classes to 0 
"logit"  1/(1 + e^{–x}) 
"none" or "identity"  x (no transformation) 
"sign"  –1 for x < 0 0 for x = 0 1 for x > 0 
"symmetric"  2x – 1 
"symmetricismax"  Sets the score for the class with the largest score to 1, and sets the scores for all other classes to –1 
"symmetriclogit"  2/(1 + e^{–x}) – 1 
For a MATLAB function or a function you define, use its function handle for the score transform. The function handle must accept a matrix (the original scores) and return a matrix of the same size (the transformed scores).
Example: "ScoreTransform","logit"
Data Types: char
 string
 function_handle
Weights
— Observation weights
nonnegative numeric vector  name of variable in Tbl
Observation weights, specified as a nonnegative numeric vector or the name of a
variable in Tbl
. The software weights each observation in
X
or Tbl
with the corresponding value in
Weights
. The length of Weights
must equal
the number of observations 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 the 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 or the response variable when training the model.
By default, Weights
is ones(n,1)
, where
n
is the number of observations in X
or
Tbl
.
The software normalizes Weights
to sum to the value of the prior
probability in the respective class.
Data Types: single
 double
 char
 string
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','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 fitckernel
by varying the parameters.
To control the crossvalidation type and other aspects of the
optimization, use the
HyperparameterOptimizationOptions
namevalue
pair argument.
Note
The values of 'OptimizeHyperparameters'
override any values you specify
using other namevalue arguments. For example, setting
'OptimizeHyperparameters'
to 'auto'
causes
fitckernel
to optimize hyperparameters corresponding to the
'auto'
option and to ignore any specified values for the
hyperparameters.
The eligible parameters for fitckernel
are:
KernelScale
—fitckernel
searches among positive values, by default logscaled in the range[1e3,1e3]
.Lambda
—fitckernel
searches among positive values, by default logscaled in the range[1e3,1e3]/n
, wheren
is the number of observations.Learner
—fitckernel
searches among'svm'
and'logistic'
.NumExpansionDimensions
—fitckernel
searches among positive integers, by default logscaled in the range[100,10000]
.Standardize
—fitckernel
searches amongtrue
andfalse
.
Set nondefault parameters by passing a vector of
optimizableVariable
objects that have nondefault
values. For example:
load fisheriris params = hyperparameters('fitckernel',meas,species); 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 the misclassification rate. 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 Classifier.
Example: 'OptimizeHyperparameters','auto'
HyperparameterOptimizationOptions
— Options for optimization
structure
Options for optimization, specified as a structure. This argument modifies the effect of the
OptimizeHyperparameters
namevalue argument. All fields in the
structure are optional.
Field Name  Values  Default 

Optimizer 
 'bayesopt' 
AcquisitionFunctionName 
Acquisition functions whose names include
 'expectedimprovementpersecondplus' 
MaxObjectiveEvaluations  Maximum number of objective function evaluations.  30 for 'bayesopt' and
'randomsearch' , and the entire grid for
'gridsearch' 
MaxTime  Time limit, specified as a positive real scalar. The time limit is in seconds, as
measured by  Inf 
NumGridDivisions  For '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 field is ignored
for categorical variables.  10 
ShowPlots  Logical value indicating whether to show plots. If true , this field plots
the best observed objective function value against the iteration number. If you
use Bayesian optimization (Optimizer is
'bayesopt' ), then this field 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 results when Optimizer is
'bayesopt' . If
true , this field overwrites a
workspace variable named
'BayesoptResults' at each
iteration. The variable is a BayesianOptimization object.  false 
Verbose  Display at the command line:
For details, see the  1 
UseParallel  Logical value indicating whether to run 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 field is The setting
 false 
Use no more than one of the following three options.  
CVPartition  A cvpartition object, as created by cvpartition  'Kfold',5 if you do not specify a crossvalidation
field 
Holdout  A scalar in the range (0,1) representing the holdout fraction  
Kfold  An integer greater than 1 
Example: 'HyperparameterOptimizationOptions',struct('MaxObjectiveEvaluations',60)
Data Types: struct
Output Arguments
Mdl
— Trained kernel classification model
ClassificationKernel
model object  ClassificationPartitionedKernel
crossvalidated model
object
Trained kernel classification model, returned as a ClassificationKernel
model object or ClassificationPartitionedKernel
crossvalidated model
object.
If you set any of the namevalue pair arguments
CrossVal
, CVPartition
,
Holdout
, KFold
, or
Leaveout
, then Mdl
is a
ClassificationPartitionedKernel
crossvalidated
classifier. Otherwise, Mdl
is a
ClassificationKernel
classifier.
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.
Note
Unlike other classification models, and for economical memory usage, a
ClassificationKernel
model object does not store
the training data or training process details (for example, convergence
history).
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 'hinge' or 'logit' depending on
the type of linear classification 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 classification 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 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.
A good practice is to examine FitInfo
to assess whether convergence is
satisfactory.
HyperparameterOptimizationResults
— Crossvalidation optimization of hyperparameters
BayesianOptimization
object  table of hyperparameters and associated values
Crossvalidation optimization of hyperparameters, returned as a BayesianOptimization
object or a table of hyperparameters and associated
values. The output is nonempty when the value of
'OptimizeHyperparameters'
is not 'none'
. The
output value depends on the Optimizer
field value of the
'HyperparameterOptimizationOptions'
namevalue pair
argument:
Value of Optimizer Field  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 classification algorithm to use 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.
The kernel classification algorithm searches for an optimal hyperplane that separates the data into two classes after mapping features into a highdimensional space. Nonlinear features that are not linearly separable in a lowdimensional space can be separable in the expanded highdimensional space. All the calculations for hyperplane classification use only dot products. You can obtain a nonlinear classification model by replacing the dot product x_{1}x_{2}' with the 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}) (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 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 scheme introduces another random
basis V instead of Z using Hadamard matrices combined
with Gaussian scaling matrices. This random basis reduces the computation cost to O(mlog
p) and reduces storage to O(m).
You can specify values for m and
σ by setting NumExpansionDimensions
and
KernelScale
, respectively, of fitckernel
.
The fitckernel
function uses the Fastfood scheme for random feature expansion and uses linear classification to train a Gaussian kernel classification model. Unlike solvers in the fitcsvm
function, which require computation of the nbyn Gram matrix, the solver in fitckernel
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 marginviolating observations, and aids in preventing 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 labels for new data. Generating C/C++ code requires MATLAB Coder™. For details, see Introduction to Code Generation.
Algorithms
fitckernel
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, typeFitInfo.Solver
in the Command Window.'LBFGSfast'
— LBFGS solver.'LBFGSblockwise'
— LBFGS solver with a blockwise strategy. Iffitckernel
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
fitckernel
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,fitckernel
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
, thenfitckernel
displays diagnostic information for each data pass and stores the information in theHistory
field ofFitInfo
.When
fitckernel
does not use a blockwise strategy, the initial estimates are zeros. If you specify'Verbose',1
, thenfitckernel
displays diagnostic information for each iteration and stores the information in theHistory
field ofFitInfo
.If you specify the
Cost
,Prior
, andWeights
namevalue arguments, the output model object stores the specified values in theCost
,Prior
, andW
properties, respectively. TheCost
property stores the userspecified cost matrix (C) without modification. ThePrior
andW
properties store the prior probabilities and observation weights, respectively, after normalization. For model training, the software updates the prior probabilities and observation weights to incorporate the penalties described in the cost matrix. For details, see Misclassification Cost Matrix, Prior Probabilities, and Observation Weights.
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.
Usage notes and limitations:
fitckernel
does not support talltable
data.Some namevalue pair arguments have different defaults compared to the default values for the inmemory
fitckernel
function. Supported namevalue pair arguments, and any differences, are:'Learner'
'NumExpansionDimensions'
'KernelScale'
'BoxConstraint'
'Lambda'
'BetaTolerance'
— Default value is relaxed to1e–3
.'GradientTolerance'
— Default value is relaxed to1e–5
.'IterationLimit'
— Default value is relaxed to20
.'BlockSize'
'RandomStream'
'HessianHistorySize'
'Verbose'
— Default value is1
.'ClassNames'
'Cost'
'Prior'
'ScoreTransform'
'Weights'
— Value must be a tall array.'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'
, thenfitckernel
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'
, thenfitckernel
might take an extra pass through the data to calculate the number of observations inX
.fitckernel
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
'HyperparameterOptimizationOptions', struct('UseParallel',true)
namevalue argument in the call to the fitckernel
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 R2017bR2023b: Kernel models support standardization of predictors
Starting in R2023b, fitckernel
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,
fitckernel
includes Standardize
as an
optimizable hyperparameter.
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