The predict , loss , margin ,
and edge methods of these classification classes
support tall arrays:  
The resume method of ClassificationKernel supports tall arrays. 
The default value for the 'IterationLimit' namevalue pair
argument is relaxed to 20 when working with tall arrays. resume uses a
blockwise strategy. For details, see Algorithms of
fitckernel .

fitcdiscr  Supported syntaxes for tall arrays return the additional output arguments
FitInfo and HyperparameterOptimizationResults .
The supported syntaxes are: [Mdl,FitInfo,HyperparameterOptimizationResults] =
fitcdiscr(Tbl,Y)
[Mdl,FitInfo,HyperparameterOptimizationResults] =
fitcdiscr(X,Y)
[Mdl,FitInfo,HyperparameterOptimizationResults] =
fitcdiscr(___,Name,Value)
The FitInfo output argument is an empty structure array currently
reserved for possible future use. The HyperparameterOptimizationResults output argument is a BayesianOptimization object or a
table of hyperparameters with associated values that describe the crossvalidation
optimization of hyperparameters. 'HyperparameterOptimizationResults' is nonempty when the
'OptimizeHyperparameters' namevalue pair argument is
nonempty at the time you create the model. The values in
'HyperparameterOptimizationResults' depend on the value
you specify for the 'HyperparameterOptimizationOptions'
namevalue pair argument when you create the model.
If you specify 'bayesopt' (default), then
HyperparameterOptimizationResults
is an object of class BayesianOptimization . If you specify 'gridsearch' or
'randomsearch' , then
HyperparameterOptimizationResults
is a table of the hyperparameters used, observed objective
function values (crossvalidation loss), and rank of
observations from lowest (best) to highest (worst).
Supported namevalue pair arguments, and any differences, are:
'ClassNames'
'Cost'
'DiscrimType'
'HyperparameterOptimizationOptions' — For
crossvalidation, tall optimization supports only
'Holdout' validation. For example, you can specify
(X,Y,'OptimizeHyperparameters','auto','HyperparameterOptimizationOptions',struct('Holdout',0.2)) .
'OptimizeHyperparameters' — The only eligible
parameter to optimize is 'DiscrimType' . Specifying
'auto' uses 'DiscrimType' .
'PredictorNames'
'Prior'
'ResponseName'
'ScoreTransform'
'Weights'
For tall arrays and tall tables, fitcdiscr returns
a CompactClassificationDiscriminant object, which
contains most of the same properties as a ClassificationDiscriminant object.
The main difference is that the compact object is sensitive to memory
requirements. The compact object does not include properties that
include the data, or that include an array of the same size as the
data. The compact object does not contain these ClassificationDiscriminant properties:
Additionally, the compact object does not support these ClassificationDiscriminant methods:
compact
crossval
cvshrink
resubEdge
resubLoss
resubMargin
resubPredict

fitcecoc 

fitckernel 
The default values for these namevalue pair arguments are different when you work
with tall arrays. 'BetaTolerance' — Default value is relaxed to
1e–3 .
'GradientTolerance' — Default value is relaxed to
1e–5 .
'IterationLimit' — Default value is relaxed to
20 .
'Verbose' — Default value is
1 .
If 'KernelScale'
is 'auto' , then fitckernel uses the random
stream controlled by tallrng for subsampling. For reproducibility, you must set a random
number seed for both the global stream and the random stream controlled by
tallrng . If 'Lambda' is 'auto' , then
fitckernel might take an extra pass through the data to
calculate the number of observations in X . fitckernel uses a blockwise strategy. For details, see Algorithms.
The crossvalidation namevalue pair arguments, which include
'CrossVal' , 'CVPartition' ,
'Holdout' , 'KFold' , and
'Leaveout' , are not supported. The 'HyperparameterOptimizationOptions' namevalue pair
argument supports only 'Holdout' for crossvalidation. For
example, you can specify
(X,Y,'OptimizeHyperparameters','auto','HyperparameterOptimizationOptions',struct('Holdout',0.2)) .

templateKernel 
The default values for these namevalue pair arguments are different when you work
with tall arrays. 'Verbose' — Default value is
1 .
'BetaTolerance' — Default value is relaxed to
1e–3 .
'GradientTolerance' — Default value is relaxed to
1e–5 .
'IterationLimit' — Default value is relaxed to
20 .
If 'KernelScale'
is 'auto' , then templateKernel uses the
random stream controlled by tallrng for subsampling. For reproducibility, you must set a random
number seed for both the global stream and the random stream controlled by
tallrng . If 'Lambda' is 'auto' , then
templateKernel might take an extra pass through the data to
calculate the number of observations. templateKernel uses a blockwise strategy. For details, see
Algorithms.

fitclinear  Some namevalue pair arguments have different defaults compared to the inmemory
fitclinear function. Supported namevalue pair arguments, and
any differences, are: 'ObservationsIn' — Supports only
'rows' .
'Lambda' — Can be 'auto'
(default) or a scalar.
'Learner'
'Regularization' — Supports only
'ridge' .
'Solver' — Supports only
'lbfgs' .
'FitBias' — Supports only
true .
'Verbose' — Default value is
1 .
'Beta'
'Bias'
'ClassNames'
'Cost'
'Prior'
'Weights' — Value must be a tall array.
'HessianHistorySize'
'BetaTolerance' — Default value is relaxed to
1e3 .
'GradientTolerance' — Default value is relaxed to
1e3 .
'IterationLimit' — Default value is relaxed to
20 .
'OptimizeHyperparameters' — Value of
'Regularization' parameter must be
'ridge' .
'HyperparameterOptimizationOptions' — For
crossvalidation, tall optimization supports only 'Holdout'
validation. For example, you can specify
(X,Y,'OptimizeHyperparameters','auto','HyperparameterOptimizationOptions',struct('Holdout',0.2)) .
For tall arrays, fitclinear implements
LBFGS by distributing the calculation of the loss and gradient among
different parts of the tall array at each iteration. Other solvers
are not available for tall arrays. When initial values for Beta and Bias are
not given, fitclinear refines the initial estimates
of the parameters by fitting the model locally to parts of the data
and combining the coefficients by averaging.

templateLinear 
The default values for these namevalue pair arguments are different when you work with tall arrays. 'Lambda' — Can be 'auto' (default)
or a scalar
'Regularization' — Supports only
'ridge'
'Solver' — Supports only
'lbfgs'
'FitBias' — Supports only
true
'Verbose' — Default value is
1
'BetaTolerance' — Default value is relaxed to
1e–3
'GradientTolerance' — Default value is relaxed to
1e–3
'IterationLimit' — Default value is relaxed to
20
When fitcecoc uses a templateLinear
object with tall arrays, the only available solver is LBFGS. The software implements
LBFGS by distributing the calculation of the loss and gradient among different parts
of the tall array at each iteration. If you do not specify initial values for
Beta and Bias , the software refines
the initial estimates of the parameters by fitting the model locally to parts of the
data and combining the coefficients by averaging.

fitcnb  
fitctree 
Supported syntaxes for tall arrays return the additional output arguments
FitInfo and
HyperparameterOptimizationResults . The
supported syntaxes are: [tree,FitInfo,HyperparameterOptimizationResults]
= fitctree(Tbl,Y)
[tree,FitInfo,HyperparameterOptimizationResults]
= fitctree(X,Y)
[tree,FitInfo,HyperparameterOptimizationResults]
= fitctree(___,Name,Value)
The FitInfo output argument is an empty structure array currently
reserved for possible future use. The HyperparameterOptimizationResults output argument is a BayesianOptimization object or a
table of hyperparameters with associated values that describe the crossvalidation
optimization of hyperparameters. 'HyperparameterOptimizationResults' is nonempty when the
'OptimizeHyperparameters' namevalue pair argument is
nonempty at the time you create the model. The values in
'HyperparameterOptimizationResults' depend on the value
you specify for the 'HyperparameterOptimizationOptions'
namevalue pair argument when you create the model.
If you specify 'bayesopt' (default), then
HyperparameterOptimizationResults
is an object of class BayesianOptimization . If you specify 'gridsearch' or
'randomsearch' , then
HyperparameterOptimizationResults
is a table of the hyperparameters used, observed objective
function values (crossvalidation loss), and rank of
observations from lowest (best) to highest (worst).
Supported namevalue pair arguments, and any
differences, are: 'AlgorithmForCategorical'
'CategoricalPredictors'
'ClassNames'
'Cost'
'HyperparameterOptimizationOptions'
— For crossvalidation, tall optimization
supports only 'Holdout'
validation. For example, you can specify
(X,Y,'OptimizeHyperparameters','auto','HyperparameterOptimizationOptions',struct('Holdout',0.2)) .
'MaxNumCategories'
'MaxNumSplits' — for tall
optimization, fitctree
searches among integers, by default logscaled in
the range
[1,max(2,min(10000,NumObservations1))] .
'MergeLeaves'
'MinLeafSize'
'MinParentSize'
'NumVariablesToSample'
'OptimizeHyperparameters'
'PredictorNames'
'Prior'
'ResponseName'
'ScoreTransform'
'SplitCriterion'
'Weights'
There is an additional namevalue pair argument specific to tall
arrays:

TreeBagger  Supported syntaxes for tall X , Y , Tbl are: B = TreeBagger(NumTrees,Tbl,Y)
B = TreeBagger(NumTrees,X,Y)
B = TreeBagger(___,Name,Value)
For tall arrays, TreeBagger supports
classification. Regression is not supported. Supported namevalue pairs are: 'NumPredictorsToSample' —
Default value is the square root of the number of variables for classification.
'MinLeafSize' — Default value is 1 if the number
of observations is less than 50,000. If the number of observations is 50,000 or
greater, then the default value is
max(1,min(5,floor(0.01*NobsChunk))) , where
NobsChunk is the number of observations in a
chunk.
'ChunkSize' (only for tall arrays)
— Default value is 50000 .
In addition, TreeBagger supports these
optional arguments of fitctree :
For tall data, TreeBagger returns
a CompactTreeBagger object
that contains most of the same properties as a full TreeBagger object.
The main difference is that the compact object is more memory efficient.
The compact object does not include properties that include the data,
or that include an array of the same size as the data. Supported CompactTreeBagger methods
are: combine
error
margin
meanMargin
predict
setDefaultYfit
The error , margin , meanMargin ,
and predict methods do not support the namevalue
pairs 'Trees' , 'TreeWeights' ,
or 'UseInstanceForTree' . The error and meanMargin methods
additionally do not support 'Weights' . TreeBagger creates a random forest
by generating trees on disjoint chunks of the data. When more data
is available than is required to create the random forest, the data
is subsampled. For a similar example, see Random Forests for Big Data (Genuer,
Poggi, TuleauMalot, VillaVialaneix 2015).
Depending on how the data is stored, it is possible that some
chunks of data contain observations from only a few classes out of
all the classes. In this case, TreeBagger might
produce inferior results compared to the case where each chunk of
data contains observations from most of the classes.
