fitcnb
Train multiclass naive Bayes model
Syntax
Description
returns
a multiclass naive Bayes model (Mdl
= fitcnb(Tbl
,ResponseVarName
)Mdl
), trained
by the predictors in table Tbl
and class labels
in the variable Tbl.ResponseVarName
.
returns
a naive Bayes classifier with additional options specified by one
or more Mdl
= fitcnb(___,Name,Value
)Name,Value
pair arguments, using any
of the previous syntaxes. For example, you can specify a distribution
to model the data, prior probabilities for the classes, or the kernel
smoothing window bandwidth.
[
also returns Mdl
,AggregateOptimizationResults
] = fitcnb(___)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.
Note
For a list of supported syntaxes when the input variables are tall arrays, see Tall Arrays.
Examples
Train a Naive Bayes Classifier
Load Fisher's iris data set.
load fisheriris
X = meas(:,3:4);
Y = species;
tabulate(Y)
Value Count Percent setosa 50 33.33% versicolor 50 33.33% virginica 50 33.33%
The software can classify data with more than two classes using naive Bayes methods.
Train a naive Bayes classifier. It is good practice to specify the class order.
Mdl = fitcnb(X,Y,'ClassNames',{'setosa','versicolor','virginica'})
Mdl = ClassificationNaiveBayes ResponseName: 'Y' CategoricalPredictors: [] ClassNames: {'setosa' 'versicolor' 'virginica'} ScoreTransform: 'none' NumObservations: 150 DistributionNames: {'normal' 'normal'} DistributionParameters: {3x2 cell}
Mdl
is a trained ClassificationNaiveBayes
classifier.
By default, the software models the predictor distribution within each class using a Gaussian distribution having some mean and standard deviation. Use dot notation to display the parameters of a particular Gaussian fit, e.g., display the fit for the first feature within setosa
.
setosaIndex = strcmp(Mdl.ClassNames,'setosa');
estimates = Mdl.DistributionParameters{setosaIndex,1}
estimates = 2×1
1.4620
0.1737
The mean is 1.4620
and the standard deviation is 0.1737
.
Plot the Gaussian contours.
figure gscatter(X(:,1),X(:,2),Y); h = gca; cxlim = h.XLim; cylim = h.YLim; hold on Params = cell2mat(Mdl.DistributionParameters); Mu = Params(2*(1:3)1,1:2); % Extract the means Sigma = zeros(2,2,3); for j = 1:3 Sigma(:,:,j) = diag(Params(2*j,:)).^2; % Create diagonal covariance matrix xlim = Mu(j,1) + 4*[1 1]*sqrt(Sigma(1,1,j)); ylim = Mu(j,2) + 4*[1 1]*sqrt(Sigma(2,2,j)); f = @(x,y) arrayfun(@(x0,y0) mvnpdf([x0 y0],Mu(j,:),Sigma(:,:,j)),x,y); fcontour(f,[xlim ylim]) % Draw contours for the multivariate normal distributions end h.XLim = cxlim; h.YLim = cylim; title('Naive Bayes Classifier  Fisher''s Iris Data') xlabel('Petal Length (cm)') ylabel('Petal Width (cm)') legend('setosa','versicolor','virginica') hold off
You can change the default distribution using the namevalue pair argument 'DistributionNames'
. For example, if some predictors are categorical, then you can specify that they are multivariate, multinomial random variables using 'DistributionNames','mvmn'
.
Specify Prior Probabilities When Training Naive Bayes Classifiers
Construct a naive Bayes classifier for Fisher's iris data set. Also, specify prior probabilities during training.
Load Fisher's iris data set.
load fisheriris X = meas; Y = species; classNames = {'setosa','versicolor','virginica'}; % Class order
X
is a numeric matrix that contains four measurements for 150 irises. Y
is a cell array of character vectors that contains the corresponding iris species.
By default, the prior class probability distribution is the relative frequency distribution of the classes in the data set. In this case the prior probability is 33% for each species. However, suppose you know that in the population 50% of the irises are setosa, 20% are versicolor, and 30% are virginica. You can incorporate this information by specifying this distribution as a prior probability during training.
Train a naive Bayes classifier. Specify the class order and prior class probability distribution.
prior = [0.5 0.2 0.3]; Mdl = fitcnb(X,Y,'ClassNames',classNames,'Prior',prior)
Mdl = ClassificationNaiveBayes ResponseName: 'Y' CategoricalPredictors: [] ClassNames: {'setosa' 'versicolor' 'virginica'} ScoreTransform: 'none' NumObservations: 150 DistributionNames: {'normal' 'normal' 'normal' 'normal'} DistributionParameters: {3x4 cell}
Mdl
is a trained ClassificationNaiveBayes
classifier, and some of its properties appear in the Command Window. The software treats the predictors as independent given a class, and, by default, fits them using normal distributions.
The naive Bayes algorithm does not use the prior class probabilities during training. Therefore, you can specify prior class probabilities after training using dot notation. For example, suppose that you want to see the difference in performance between a model that uses the default prior class probabilities and a model that uses different prior
.
Create a new naive Bayes model based on Mdl
, and specify that the prior class probability distribution is an empirical class distribution.
defaultPriorMdl = Mdl; FreqDist = cell2table(tabulate(Y)); defaultPriorMdl.Prior = FreqDist{:,3};
The software normalizes the prior class probabilities to sum to 1
.
Estimate the crossvalidation error for both models using 10fold crossvalidation.
rng(1); % For reproducibility
defaultCVMdl = crossval(defaultPriorMdl);
defaultLoss = kfoldLoss(defaultCVMdl)
defaultLoss = 0.0533
CVMdl = crossval(Mdl); Loss = kfoldLoss(CVMdl)
Loss = 0.0340
Mdl
performs better than defaultPriorMdl
.
Specify Predictor Distributions for Naive Bayes Classifiers
Load Fisher's iris data set.
load fisheriris
X = meas;
Y = species;
Train a naive Bayes classifier using every predictor. It is good practice to specify the class order.
Mdl1 = fitcnb(X,Y,... 'ClassNames',{'setosa','versicolor','virginica'})
Mdl1 = ClassificationNaiveBayes ResponseName: 'Y' CategoricalPredictors: [] ClassNames: {'setosa' 'versicolor' 'virginica'} ScoreTransform: 'none' NumObservations: 150 DistributionNames: {'normal' 'normal' 'normal' 'normal'} DistributionParameters: {3x4 cell}
Mdl1.DistributionParameters
ans=3×4 cell array
{2x1 double} {2x1 double} {2x1 double} {2x1 double}
{2x1 double} {2x1 double} {2x1 double} {2x1 double}
{2x1 double} {2x1 double} {2x1 double} {2x1 double}
Mdl1.DistributionParameters{1,2}
ans = 2×1
3.4280
0.3791
By default, the software models the predictor distribution within each class as a Gaussian with some mean and standard deviation. There are four predictors and three class levels. Each cell in Mdl1.DistributionParameters
corresponds to a numeric vector containing the mean and standard deviation of each distribution, e.g., the mean and standard deviation for setosa iris sepal widths are 3.4280
and 0.3791
, respectively.
Estimate the confusion matrix for Mdl1
.
isLabels1 = resubPredict(Mdl1); ConfusionMat1 = confusionchart(Y,isLabels1);
Element (j, k) of the confusion matrix chart represents the number of observations that the software classifies as k, but are truly in class j according to the data.
Retrain the classifier using the Gaussian distribution for predictors 1 and 2 (the sepal lengths and widths), and the default normal kernel density for predictors 3 and 4 (the petal lengths and widths).
Mdl2 = fitcnb(X,Y,... 'DistributionNames',{'normal','normal','kernel','kernel'},... 'ClassNames',{'setosa','versicolor','virginica'}); Mdl2.DistributionParameters{1,2}
ans = 2×1
3.4280
0.3791
The software does not train parameters to the kernel density. Rather, the software chooses an optimal width. However, you can specify a width using the 'Width'
namevalue pair argument.
Estimate the confusion matrix for Mdl2
.
isLabels2 = resubPredict(Mdl2); ConfusionMat2 = confusionchart(Y,isLabels2);
Based on the confusion matrices, the two classifiers perform similarly in the training sample.
Compare Classifiers Using CrossValidation
Load Fisher's iris data set.
load fisheriris X = meas; Y = species; rng(1); % For reproducibility
Train and crossvalidate a naive Bayes classifier using the default options and kfold crossvalidation. It is good practice to specify the class order.
CVMdl1 = fitcnb(X,Y,... 'ClassNames',{'setosa','versicolor','virginica'},... 'CrossVal','on');
By default, the software models the predictor distribution within each class as a Gaussian with some mean and standard deviation. CVMdl1
is a ClassificationPartitionedModel
model.
Create a default naive Bayes binary classifier template, and train an errorcorrecting, output codes multiclass model.
t = templateNaiveBayes(); CVMdl2 = fitcecoc(X,Y,'CrossVal','on','Learners',t);
CVMdl2
is a ClassificationPartitionedECOC
model. You can specify options for the naive Bayes binary learners using the same namevalue pair arguments as for fitcnb
.
Compare the outofsample kfold classification error (proportion of misclassified observations).
classErr1 = kfoldLoss(CVMdl1,'LossFun','ClassifErr')
classErr1 = 0.0533
classErr2 = kfoldLoss(CVMdl2,'LossFun','ClassifErr')
classErr2 = 0.0467
Mdl2
has a lower generalization error.
Train Naive Bayes Classifiers Using Multinomial Predictors
Some spam filters classify an incoming email as spam based on how many times a word or punctuation (called tokens) occurs in an email. The predictors are the frequencies of particular words or punctuations in an email. Therefore, the predictors compose multinomial random variables.
This example illustrates classification using naive Bayes and multinomial predictors.
Create Training Data
Suppose you observed 1000 emails and classified them as spam or not spam. Do this by randomly assigning 1 or 1 to y
for each email.
n = 1000; % Sample size rng(1); % For reproducibility Y = randsample([1 1],n,true); % Random labels
To build the predictor data, suppose that there are five tokens in the vocabulary, and 20 observed tokens per email. Generate predictor data from the five tokens by drawing random, multinomial deviates. The relative frequencies for tokens corresponding to spam emails should differ from emails that are not spam.
tokenProbs = [0.2 0.3 0.1 0.15 0.25;... 0.4 0.1 0.3 0.05 0.15]; % Token relative frequencies tokensPerEmail = 20; % Fixed for convenience X = zeros(n,5); X(Y == 1,:) = mnrnd(tokensPerEmail,tokenProbs(1,:),sum(Y == 1)); X(Y == 1,:) = mnrnd(tokensPerEmail,tokenProbs(2,:),sum(Y == 1));
Train the Classifier
Train a naive Bayes classifier. Specify that the predictors are multinomial.
Mdl = fitcnb(X,Y,'DistributionNames','mn');
Mdl
is a trained ClassificationNaiveBayes
classifier.
Assess the insample performance of Mdl
by estimating the misclassification error.
isGenRate = resubLoss(Mdl,'LossFun','ClassifErr')
isGenRate = 0.0200
The insample misclassification rate is 2%.
Create New Data
Randomly generate deviates that represent a new batch of emails.
newN = 500; newY = randsample([1 1],newN,true); newX = zeros(newN,5); newX(newY == 1,:) = mnrnd(tokensPerEmail,tokenProbs(1,:),... sum(newY == 1)); newX(newY == 1,:) = mnrnd(tokensPerEmail,tokenProbs(2,:),... sum(newY == 1));
Assess Classifier Performance
Classify the new emails using the trained naive Bayes classifier Mdl
, and determine whether the algorithm generalizes.
oosGenRate = loss(Mdl,newX,newY)
oosGenRate = 0.0261
The outofsample misclassification rate is 2.6% indicating that the classifier generalizes fairly well.
Optimize Naive Bayes Classifier
This example shows how to use the OptimizeHyperparameters
namevalue pair to minimize crossvalidation loss in a naive Bayes classifier using fitcnb
. The example uses Fisher's iris data.
Load Fisher's iris data.
load fisheriris X = meas; Y = species; classNames = {'setosa','versicolor','virginica'};
Optimize the classification using the 'auto'
parameters.
For reproducibility, set the random seed and use the 'expectedimprovementplus'
acquisition function.
rng default Mdl = fitcnb(X,Y,'ClassNames',classNames,'OptimizeHyperparameters','auto',... 'HyperparameterOptimizationOptions',struct('AcquisitionFunctionName',... 'expectedimprovementplus'))
====================================================================================================================  Iter  Eval  Objective  Objective  BestSoFar  BestSoFar  Distribution Width  Standardize    result   runtime  (observed)  (estim.)  Names    ====================================================================================================================  1  Best  0.093333  0.82584  0.093333  0.093333  kernel  5.6939  false   2  Accept  0.13333  0.21335  0.093333  0.11333  kernel  94.849  true   3  Best  0.053333  0.12186  0.053333  0.05765  normal       4  Accept  0.053333  0.061748  0.053333  0.053336  normal       5  Accept  0.26667  0.28622  0.053333  0.053338  kernel  0.001001  true   6  Accept  0.093333  0.2937  0.053333  0.053337  kernel  10.043  false   7  Accept  0.26667  0.22145  0.053333  0.05334  kernel  0.0010132  false   8  Accept  0.093333  0.17161  0.053333  0.053338  kernel  985.05  false   9  Accept  0.13333  0.14985  0.053333  0.053338  kernel  993.63  true   10  Accept  0.053333  0.082901  0.053333  0.053336  normal       11  Accept  0.053333  0.093469  0.053333  0.053336  normal       12  Best  0.046667  0.19699  0.046667  0.046679  kernel  0.30205  true   13  Accept  0.11333  0.30521  0.046667  0.046685  kernel  1.3021  true   14  Accept  0.053333  0.14629  0.046667  0.046695  kernel  0.10521  true   15  Accept  0.046667  0.23372  0.046667  0.046677  kernel  0.25016  false   16  Accept  0.06  0.28687  0.046667  0.046686  kernel  0.58328  false   17  Accept  0.046667  0.27002  0.046667  0.046656  kernel  0.07969  false   18  Accept  0.093333  0.39787  0.046667  0.046654  kernel  131.33  false   19  Accept  0.046667  0.44011  0.046667  0.04648  kernel  0.13384  false   20  Best  0.04  0.2181  0.04  0.040132  kernel  0.19525  true  ====================================================================================================================  Iter  Eval  Objective  Objective  BestSoFar  BestSoFar  Distribution Width  Standardize    result   runtime  (observed)  (estim.)  Names    ====================================================================================================================  21  Accept  0.04  0.49001  0.04  0.040066  kernel  0.19458  true   22  Accept  0.04  0.64534  0.04  0.040043  kernel  0.19601  true   23  Accept  0.04  0.63292  0.04  0.040031  kernel  0.19412  true   24  Accept  0.10667  0.64097  0.04  0.040018  kernel  0.0084391  true   25  Accept  0.073333  0.64437  0.04  0.040022  kernel  0.02769  false   26  Accept  0.04  0.21916  0.04  0.04002  kernel  0.2037  true   27  Accept  0.13333  0.14582  0.04  0.040021  kernel  12.501  true   28  Accept  0.11333  0.13708  0.04  0.040006  kernel  0.0048728  false   29  Accept  0.1  0.16268  0.04  0.039993  kernel  0.028653  true   30  Accept  0.046667  0.61507  0.04  0.041008  kernel  0.18725  true  __________________________________________________________ Optimization completed. MaxObjectiveEvaluations of 30 reached. Total function evaluations: 30 Total elapsed time: 20.1355 seconds Total objective function evaluation time: 9.3506 Best observed feasible point: DistributionNames Width Standardize _________________ _______ ___________ kernel 0.19525 true Observed objective function value = 0.04 Estimated objective function value = 0.041117 Function evaluation time = 0.2181 Best estimated feasible point (according to models): DistributionNames Width Standardize _________________ ______ ___________ kernel 0.2037 true Estimated objective function value = 0.041008 Estimated function evaluation time = 0.30076
Mdl = ClassificationNaiveBayes ResponseName: 'Y' CategoricalPredictors: [] ClassNames: {'setosa' 'versicolor' 'virginica'} ScoreTransform: 'none' NumObservations: 150 HyperparameterOptimizationResults: [1x1 BayesianOptimization] DistributionNames: {'kernel' 'kernel' 'kernel' 'kernel'} DistributionParameters: {3x4 cell} Kernel: {'normal' 'normal' 'normal' 'normal'} Support: {'unbounded' 'unbounded' 'unbounded' 'unbounded'} Width: [3x4 double] Mu: [5.8433 3.0573 3.7580 1.1993] Sigma: [0.8281 0.4359 1.7653 0.7622]
Input Arguments
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
.
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
Y
— Class labels
categorical array  character array  string array  logical vector  numeric vector  cell array of character vectors
Class labels to which the naive Bayes classifier is trained, specified as a categorical,
character, or string array, a logical or numeric vector, or a cell array of character
vectors. Each element of Y
defines the class membership of the
corresponding row of X
. Y
supports
K class levels.
If Y
is a character array, then each row
must correspond to one class label.
The length of Y
and the number of rows of X
must
be equivalent.
Data Types: categorical
 char
 string
 logical
 single
 double
 cell
X
— Predictor data
numeric matrix
Predictor data, specified as a numeric matrix.
Each row of X
corresponds to one observation
(also known as an instance or example), and each column corresponds
to one variable (also known as a feature).
The length of Y
and the number of rows of X
must
be equivalent.
Data Types: double
Note:
The software treats NaN
, empty character vector (''
),
empty string (""
), <missing>
, and
<undefined>
elements as missing data values.
If
Y
contains missing values, then the software removes them and the corresponding rows ofX
.If
X
contains any rows composed entirely of missing values, then the software removes those rows and the corresponding elements ofY
.If
X
contains missing values and you set'DistributionNames','mn'
, then the software removes those rows ofX
and the corresponding elements ofY
.If a predictor is not represented in a class, that is, if all of its values are
NaN
within a class, then the software returns an error.
Removing rows of X
and corresponding elements of
Y
decreases the effective training or crossvalidation sample
size.
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: 'DistributionNames','mn','Prior','uniform','KSWidth',0.5
specifies that the data distribution is multinomial, the prior probabilities for all
classes are equal, and the kernel smoothing window bandwidth for all classes is
0.5
units.
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.
DistributionNames
— Data distributions
'kernel'
 'mn'
 'mvmn'
 'normal'
 string array  cell array of character vectors
Data distributions fitcnb
uses to model the data, specified as the
commaseparated pair consisting of 'DistributionNames'
and a
character vector or string scalar, a string array, or a cell array of character vectors
with values from this table.
Value  Description 

'kernel'  Kernel smoothing density estimate. 
'mn'  Multinomial distribution. If you specify mn ,
then all features are components of a multinomial distribution.
Therefore, you cannot include 'mn' as an element
of a string array or a cell array of character vectors. For details,
see Algorithms. 
'mvmn'  Multivariate multinomial distribution. For details, see Algorithms. 
'normal'  Normal (Gaussian) distribution. 
If you specify a character vector or string scalar, then the software models all the features using that distribution. If you specify a 1byP string array or cell array of character vectors, then the software models feature j using the distribution in element j of the array.
By default, the software sets all predictors specified as categorical
predictors (using the CategoricalPredictors
namevalue
pair argument) to 'mvmn'
. Otherwise, the default
distribution is 'normal'
.
You must specify that at least one predictor has distribution 'kernel'
to
additionally specify Kernel
, Standardize
,
Support
, or Width
.
Example: 'DistributionNames','mn'
Example: 'DistributionNames',{'kernel','normal','kernel'}
Kernel
— Kernel smoother type
'normal'
(default)  'box'
 'epanechnikov'
 'triangle'
 string array  cell array of character vectors
Kernel smoother type, specified as the commaseparated pair consisting of
'Kernel'
and a character vector or string scalar, a string array,
or a cell array of character vectors.
This table summarizes the available options for setting the kernel smoothing density region. Let I{u} denote the indicator function.
Value  Kernel  Formula 

'box'  Box (uniform) 
$$f(x)=0.5I\left\{\leftx\right\le 1\right\}$$ 
'epanechnikov'  Epanechnikov 
$$f(x)=0.75\left(1{x}^{2}\right)I\left\{\leftx\right\le 1\right\}$$ 
'normal'  Gaussian 
$$f(x)=\frac{1}{\sqrt{2\pi}}\mathrm{exp}\left(0.5{x}^{2}\right)$$ 
'triangle'  Triangular 
$$f(x)=\left(1\leftx\right\right)I\left\{\leftx\right\le 1\right\}$$ 
If you specify a 1byP string array or cell array, with each element of
the array containing any value in the table, then the software trains the classifier
using the kernel smoother type in element j for feature
j in X
. The software ignores elements of
Kernel
not corresponding to a predictor whose distribution is
'kernel'
.
You must specify that at least one predictor has distribution 'kernel'
to
additionally specify Kernel
, Standardize
,
Support
, or Width
.
Example: 'Kernel',{'epanechnikov','normal'}
Standardize
— Flag to standardize kerneldistributed predictors
false
or 0
(default)  true
or 1
Since R2023b
Flag to standardize the kerneldistributed predictors, specified as a numeric or
logical 0
(false
) or 1
(true
). This argument is valid only when the
DistributionNames
value contains at least one kernel
distribution ("kernel"
).
If you set Standardize
to true
, then the
software centers and scales each kerneldistributed predictor variable by the
corresponding column mean and standard deviation. The software does not standardize
predictors with nonkernel distributions, such as categorical predictors.
Example: "Standardize",true
Data Types: single
 double
 logical
Support
— Kernel smoothing density support
'unbounded'
(default)  'positive'
 string array  cell array  numeric row vector
Kernel smoothing density support, specified as the commaseparated pair consisting of
'Support'
and 'positive'
,
'unbounded'
, a string array, a cell array, or a numeric row
vector. The software applies the kernel smoothing density to the specified
region.
This table summarizes the available options for setting the kernel smoothing density region.
Value  Description 

1by2 numeric row vector  For example, [L,U] , where L and U are
the finite lower and upper bounds, respectively, for the density support. 
'positive'  The density support is all positive real values. 
'unbounded'  The density support is all real values. 
If you specify a 1byP string array or cell array, with
each element in the string array containing any text value in the table and each element
in the cell array containing any value in the table, then the software trains the
classifier using the kernel support in element j for feature
j in X
. The software ignores elements of
Kernel
not corresponding to a predictor whose distribution is
'kernel'
.
You must specify that at least one predictor has distribution 'kernel'
to
additionally specify Kernel
, Standardize
,
Support
, or Width
.
Example: 'Support',{[10,20],'unbounded'}
Data Types: char
 string
 cell
 double
Width
— Kernel smoothing window width
matrix of numeric values  numeric column vector  numeric row vector  scalar
Kernel smoothing window width, specified as the commaseparated
pair consisting of 'Width'
and a matrix of numeric
values, numeric column vector, numeric row vector, or scalar.
Suppose there are K class levels and P predictors. This table summarizes the available options for setting the kernel smoothing window width.
Value  Description 

KbyP matrix of numeric values  Element (k,j) specifies the width for predictor j in class k. 
Kby1 numeric column vector  Element k specifies the width for all predictors in class k. 
1byP numeric row vector  Element j specifies the width in all class levels for predictor j. 
scalar  Specifies the bandwidth for all features in all classes. 
By default, the software selects
a default width automatically for each combination of predictor and
class by using a value that is optimal for a Gaussian distribution.
If you specify Width
and it contains NaN
s,
then the software selects widths for the elements containing NaN
s.
You must specify that at least one predictor has distribution 'kernel'
to
additionally specify Kernel
, Standardize
,
Support
, or Width
.
Example: 'Width',[NaN NaN]
Data Types: double
 struct
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.
To override this crossvalidation setting, use one of these namevalue
pair arguments: CVPartition
,
Holdout
, KFold
, or
Leaveout
. To create a crossvalidated model,
you can use one crossvalidation namevalue pair argument at a time
only.
Alternatively, crossvalidate later by passing
Mdl
to crossval
.
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 "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, specified in the
NumObservations
property of the model), the software completes
these steps:
Reserve the one observation as validation data, and train the model using the other n – 1 observations.
Store the n compact trained models in an nby1 cell vector 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: Leaveout="on"
Data Types: char
 string
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
), fitcnb
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
), fitcnb
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, fitcnb
uses multivariate multinomial distributions. For details, see
DistributionNames
and Algorithms.
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
— Cost of misclassification
square matrix  structure
Cost of misclassification of a point, specified as the commaseparated
pair consisting of 'Cost'
and one of the
following:
Square matrix, where
Cost(i,j)
is the cost of classifying a point into classj
if its true class isi
(i.e., 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
, additionally specify theClassNames
namevalue pair argument.Structure
S
having two fields:S.ClassNames
containing the group names as a variable of the same type asY
, andS.ClassificationCosts
containing the cost matrix.
The default is Cost(i,j)=1
if
i~=j
, and Cost(i,j)=0
if
i=j
.
Example: 'Cost',struct('ClassNames',{{'b','g'}},'ClassificationCosts',[0
0.5; 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,fitcnb
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'
 vector of scalar values  structure
Prior probabilities for each class, specified as the commaseparated
pair consisting of 'Prior'
and a value in this
table.
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 Mdl .ClassNames or
specify the order using the ClassNames namevalue
pair argument. The software normalizes the elements such that they
sum to 1 . 
structure  A structure

If you set values for both Weights
and Prior
,
the weights are renormalized to add up to the value of the prior probability
in the respective class.
Example: 'Prior','uniform'
Data Types: char
 string
 single
 double
 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
numeric vector of positive values  name of variable in Tbl
Observation weights, specified as the commaseparated pair consisting
of 'Weights'
and a numeric vector of positive values
or name of a variable in Tbl
. The software weighs
the observations in each row of X
or Tbl
with
the corresponding value in Weights
. The size of Weights
must
equal the number of rows of 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 when training the model.
The software normalizes Weights
to sum up
to the value of the prior probability in the respective class.
By default, Weights
is ones(
,
where n
,1)n
is the number of observations in X
or Tbl
.
Data Types: double
 single
 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
the following:
'none'
— Do not optimize.'auto'
— Use{'DistributionNames','Standardize','Width'}
.'all'
— Optimize all eligible parameters.String array or cell array of eligible parameter names.
Vector of
optimizableVariable
objects, typically the output ofhyperparameters
.
The optimization attempts to minimize the crossvalidation loss
(error) for fitcnb
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
fitcnb
to optimize hyperparameters corresponding to the
"auto"
option and to ignore any specified values for the
hyperparameters.
The eligible parameters for fitcnb
are:
DistributionNames
—fitcnb
searches among'normal'
and'kernel'
.Kernel
—fitcnb
searches among'normal'
,'box'
,'epanechnikov'
, and'triangle'
.Standardize
—fitcnb
searches amongtrue
andfalse
.Width
—fitcnb
searches among real values, by default logscaled in the range[1e3,1e3]
.
Set nondefault parameters by passing a vector of
optimizableVariable
objects that have nondefault
values. For example,
load fisheriris params = hyperparameters('fitcnb',meas,species); params(2).Range = [1e2,1e2];
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
option of the
HyperparameterOptimizationOptions
namevalue argument. To control the
plots, set the ShowPlots
field of the
HyperparameterOptimizationOptions
namevalue argument.
For an example, see Optimize Naive Bayes Classifier.
Example: '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 naive Bayes classification model
ClassificationNaiveBayes
model object  ClassificationPartitionedModel
crossvalidated
model object
Trained naive Bayes classification model, returned as a ClassificationNaiveBayes
model
object or a ClassificationPartitionedModel
crossvalidated
model object.
If you set any of the namevalue pair arguments KFold
, Holdout
, CrossVal
,
or CVPartition
, then Mdl
is
a ClassificationPartitionedModel
crossvalidated
model object. Otherwise, Mdl
is a ClassificationNaiveBayes
model
object.
To reference properties of Mdl
, use dot notation.
For example, to access the estimated distribution parameters, enter Mdl.DistributionParameters
.
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.
More About
BagofTokens Model
In the bagoftokens model, the value of predictor j is the nonnegative number of occurrences of token j in the observation. The number of categories (bins) in the multinomial model is the number of distinct tokens (number of predictors).
Naive Bayes
Naive Bayes is a classification algorithm that applies density estimation to the data.
The algorithm leverages Bayes theorem, and (naively) assumes that the predictors are conditionally independent, given the class. Although the assumption is usually violated in practice, naive Bayes classifiers tend to yield posterior distributions that are robust to biased class density estimates, particularly where the posterior is 0.5 (the decision boundary) [1].
Naive Bayes classifiers assign observations to the most probable class (in other words, the maximum a posteriori decision rule). Explicitly, the algorithm takes these steps:
Estimate the densities of the predictors within each class.
Model posterior probabilities according to Bayes rule. That is, for all k = 1,...,K,
$$\widehat{P}\left(Y=k{X}_{1},\mathrm{..},{X}_{P}\right)=\frac{\pi \left(Y=k\right){\displaystyle \prod _{j=1}^{P}P}\left({X}_{j}Y=k\right)}{{\displaystyle \sum}_{k=1}^{K}\pi \left(Y=k\right){\displaystyle \prod _{j=1}^{P}P}\left({X}_{j}Y=k\right)},$$
where:
Y is the random variable corresponding to the class index of an observation.
X_{1},...,X_{P} are the random predictors of an observation.
$$\pi \left(Y=k\right)$$ is the prior probability that a class index is k.
Classify an observation by estimating the posterior probability for each class, and then assign the observation to the class yielding the maximum posterior probability.
If the predictors compose a multinomial distribution, then the posterior probability$$\widehat{P}\left(Y=k{X}_{1},\mathrm{..},{X}_{P}\right)\propto \pi \left(Y=k\right){P}_{mn}\left({X}_{1},\mathrm{...},{X}_{P}Y=k\right),$$ where $${P}_{mn}\left({X}_{1},\mathrm{...},{X}_{P}Y=k\right)$$ is the probability mass function of a multinomial distribution.
Tips
For classifying countbased data, such as the bagoftokens model, use the multinomial distribution (e.g., set
'DistributionNames','mn'
).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
If predictor variable
j
has a conditional normal distribution (see theDistributionNames
namevalue argument), the software fits the distribution to the data by computing the classspecific weighted mean and the unbiased estimate of the weighted standard deviation. For each class k:The weighted mean of predictor j is
$${\overline{x}}_{jk}=\frac{{\displaystyle \sum _{\{i:{y}_{i}=k\}}{w}_{i}{x}_{ij}}}{{\displaystyle \sum _{\{i:{y}_{i}=k\}}{w}_{i}}},$$
where w_{i} is the weight for observation i. The software normalizes weights within a class such that they sum to the prior probability for that class.
The unbiased estimator of the weighted standard deviation of predictor j is
$${s}_{jk}={\left[\frac{{\displaystyle \sum _{\{i:{y}_{i}=k\}}{w}_{i}{\left({x}_{ij}{\overline{x}}_{jk}\right)}^{2}}}{{z}_{1k}\frac{{z}_{2k}}{{z}_{1k}}}\right]}^{1/2},$$
where z_{1k} is the sum of the weights within class k and z_{2k} is the sum of the squared weights within class k.
If all predictor variables compose a conditional multinomial distribution (you specify
'DistributionNames','mn'
), the software fits the distribution using the bagoftokens model. The software stores the probability that tokenj
appears in classk
in the propertyDistributionParameters{
. Using additive smoothing [2], the estimated probability isk
,j
}$$P(\text{token}j\text{class}k)=\frac{1+{c}_{jk}}{P+{c}_{k}},$$
where:
$${c}_{jk}={n}_{k}\frac{{\displaystyle \sum _{\{i:{y}_{i}=k\}}^{}{x}_{ij}}{w}_{i}^{}}{{\displaystyle \sum _{\{i:{y}_{i}=k\}}^{}{w}_{i}}},$$ which is the weighted number of occurrences of token j in class k.
n_{k} is the number of observations in class k.
$${w}_{i}^{}$$ is the weight for observation i. The software normalizes weights within a class such that they sum to the prior probability for that class.
$${c}_{k}={\displaystyle \sum _{j=1}^{P}{c}_{jk}},$$ which is the total weighted number of occurrences of all tokens in class k.
If predictor variable
j
has a conditional multivariate multinomial distribution:The software collects a list of the unique levels, stores the sorted list in
CategoricalLevels
, and considers each level a bin. Each predictor/class combination is a separate, independent multinomial random variable.For each class
k
, the software counts instances of each categorical level using the list stored inCategoricalLevels{
.j
}The software stores the probability that predictor
j
, in classk
, has level L in the propertyDistributionParameters{
, for all levels ink
,j
}CategoricalLevels{
. Using additive smoothing [2], the estimated probability isj
}$$P\left(\text{predictor}j=L\text{class}k\right)=\frac{1+{m}_{jk}(L)}{{m}_{j}+{m}_{k}},$$
where:
$${m}_{jk}(L)={n}_{k}\frac{{\displaystyle \sum _{\{i:{y}_{i}=k\}}^{}I\{{x}_{ij}=L\}{w}_{i}^{}}}{{\displaystyle \sum _{\{i:{y}_{i}=k\}}^{}{w}_{i}^{}}},$$ which is the weighted number of observations for which predictor j equals L in class k.
n_{k} is the number of observations in class k.
$$I\left\{{x}_{ij}=L\right\}=1$$ if x_{ij} = L, 0 otherwise.
$${w}_{i}^{}$$ is the weight for observation i. The software normalizes weights within a class such that they sum to the prior probability for that class.
m_{j} is the number of distinct levels in predictor j.
m_{k} is the weighted number of observations in class k.
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 as is. ThePrior
andW
properties store the prior probabilities and observation weights, respectively, after normalization. For details, see Misclassification Cost Matrix, Prior Probabilities, and Observation Weights.The software uses the
Cost
property for prediction, but not training. Therefore,Cost
is not readonly; you can change the property value by using dot notation after creating the trained model.
References
[1] Hastie, T., R. Tibshirani, and J. Friedman. The Elements of Statistical Learning, Second Edition. NY: Springer, 2008.
[2] Manning, Christopher D., Prabhakar Raghavan, and Hinrich Schütze. Introduction to Information Retrieval, NY: Cambridge University Press, 2008.
Extended Capabilities
Tall Arrays
Calculate with arrays that have more rows than fit in memory.
This function supports tall arrays with the limitations:
Supported syntaxes are:
Mdl = fitcnb(Tbl,Y)
Mdl = fitcnb(X,Y)
Mdl = fitcnb(___,Name,Value)
[Mdl,AggregateOptimizationResults] = fitcnb(___,Name=Value)
—fitcnb
additionally returns theAggregateBayesianOptimization
objectAggregateOptimizationResults
, which contains hyperparameter optimization results when you specify theOptimizeHyperparameters
andHyperparameterOptimizationOptions
namevalue arguments. You must also specify theConstraintType
andConstraintBounds
options ofHyperparameterOptimizationOptions
.
Options related to kernel densities, crossvalidation, and hyperparameter optimization are not supported. The supported namevalue pair arguments are:
'DistributionNames'
—'kernel'
value is not supported.'CategoricalPredictors'
'Cost'
'PredictorNames'
'Prior'
'ResponseName'
'ScoreTransform'
'Weights'
— Value must be a tall array.
For more information, see Tall Arrays for OutofMemory Data.
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 fitcnb
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 R2014bR2023b: Naive Bayes models support standardization of kerneldistributed predictors
Starting in R2023b, fitcnb
supports the standardization
of predictors with kernel distributions. That is, you can specify the
Standardize
namevalue argument as true
when the DistributionNames
namevalue argument includes at
least one "kernel"
distribution.
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,
fitcnb
includes Standardize
as an
optimizable hyperparameter.
R2023b: Width
hyperparameter search range does not depend on predictor data during optimization of naive Bayes models
Starting in R2023b, fitcnb
optimizes the kernel smoothing window width of naive Bayes models by using the default search range [1e3,1e3]
. That is, when you specify to optimize the naive Bayes hyperparameter Width
by using the OptimizeHyperparameters
namevalue argument, the function searches among positive values logscaled in the range [1e3,1e3]
.
In previous releases, the default search range for the Width
hyperparameter was
[MinPredictorDiff/4,max(MaxPredictorRange,MinPredictorDiff)]
, where
MinPredictorDiff
and MaxPredictorRange
were
determined as follows:
diffs = diff(sort(X));
MinPredictorDiff = min(diffs(diffs ~= 0),[],"omitnan");
MaxPredictorRange = max(max(X)  min(X));
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