Predict labels using naive Bayes classification model

`[`

also returns:`label`

,`Posterior`

,`Cost`

]
= predict(`Mdl`

,`X`

)

A matrix of posterior probabilities (

`Posterior`

) indicating the likelihood that a label comes from a particular class.A matrix of misclassification costs (

`Cost`

). For each observation in`X`

, the predicted class label corresponds to the minimum expected classification costs among all classes.

`Mdl`

— Naive Bayes classifier`ClassificationNaiveBayes`

model | `CompactClassificationNaiveBayes`

modelNaive Bayes classifier, specified as a `ClassificationNaiveBayes`

model
or `CompactClassificationNaiveBayes`

model
returned by `fitcnb`

or `compact`

,
respectively.

`X`

— Predictor data to be classifiednumeric matrix | table

Predictor data to be classified, specified as a numeric matrix or table.

Each row of `X`

corresponds to one observation, and each
column corresponds to one variable.

For a numeric matrix:

The variables making up the columns of

`X`

must have the same order as the predictor variables that trained`Mdl`

.If you trained

`Mdl`

using a table (for example,`Tbl`

), then`X`

can be a numeric matrix if`Tbl`

contains all numeric predictor variables. To treat numeric predictors in`Tbl`

as categorical during training, identify categorical predictors using the`CategoricalPredictors`

name-value pair argument of`fitcnb`

. If`Tbl`

contains heterogeneous predictor variables (for example, numeric and categorical data types) and`X`

is a numeric matrix, then`predict`

throws an error.

For a table:

`predict`

does not support multi-column variables and cell arrays other than cell arrays of character vectors.If you trained

`Mdl`

using a table (for example,`Tbl`

), then all predictor variables in`X`

must have the same variable names and data types as those that trained`Mdl`

(stored in`Mdl.PredictorNames`

). However, the column order of`X`

does not need to correspond to the column order of`Tbl`

.`Tbl`

and`X`

can contain additional variables (response variables, observation weights, etc.), but`predict`

ignores them.If you trained

`Mdl`

using a numeric matrix, then the predictor names in`Mdl.PredictorNames`

and corresponding predictor variable names in`X`

must be the same. To specify predictor names during training, see the`PredictorNames`

name-value pair argument of`fitcnb`

. All predictor variables in`X`

must be numeric vectors.`X`

can contain additional variables (response variables, observation weights, etc.), but`predict`

ignores them.

**Data Types: **`table`

| `double`

| `single`

If

`Mdl.DistributionNames`

is`'mn'`

, then the software returns`NaN`

s corresponding to rows of`X`

containing at least one`NaN`

.If

`Mdl.DistributionNames`

is not`'mn'`

, then the software ignores`NaN`

values when estimating misclassification costs and posterior probabilities. Specifically, the software computes the conditional density of the predictors given the class by leaving out the factors corresponding to missing predictor values.For predictor distribution specified as

`'mvmn'`

, if`X`

contains levels that are not represented in the training data (i.e., not in`Mdl.CategoricalLevels`

for that predictor), then the conditional density of the predictors given the class is 0. For those observations, the software returns the corresponding value of`Posterior`

as a`NaN`

. The software determines the class label for such observations using the class prior probability, stored in`Mdl.Prior`

.

`label`

— Predicted class labelscategorical vector | character array | logical vector | numeric vector | cell array of character vectors

Predicted class labels, returned as a categorical vector, character array, logical or numeric vector, or cell array of character vectors.

`label`

:

`Posterior`

— Class posterior probabilitiesnumeric matrix

Class posterior
probabilities, returned as a numeric matrix.
`Posterior`

has rows equal to the number of rows of
`X`

and columns equal to the number of distinct
classes in the training data
(`size(Mdl.ClassNames,1)`

).

`Posterior(j,k)`

is the predicted posterior probability
of class `k`

(i.e., in class
`Mdl.ClassNames(k)`

) given the observation in row
`j`

of `X`

.

**Data Types: **`double`

`Cost`

— Expected misclassification costsnumeric matrix

Expected misclassification
costs, returned as a numeric matrix. `Cost`

has
rows equal to the number of rows of `X`

and columns equal
to the number of distinct classes in the training data
(`size(Mdl.ClassNames,1)`

).

`Cost(j,k)`

is the expected misclassification cost of the
observation in row `j`

of `X`

being
predicted into class `k`

(i.e., in class
`Mdl.ClassNames(k)`

).

Load Fisher's iris data set.

load fisheriris X = meas; % Predictors Y = species; % Response rng(1);

Train a naive Bayes classifier and specify to holdout 30% of the data for a test sample. It is good practice to specify the class order. Assume that each predictor is conditionally, normally distributed given its label.

CVMdl = fitcnb(X,Y,'Holdout',0.30,... 'ClassNames',{'setosa','versicolor','virginica'}); CMdl = CVMdl.Trained{1}; % Extract trained, compact classifier testIdx = test(CVMdl.Partition); % Extract the test indices XTest = X(testIdx,:); YTest = Y(testIdx);

`CVMdl`

is a `ClassificationPartitionedModel`

classifier. It contains the property `Trained`

, which is a 1-by-1 cell array holding a `CompactClassificationNaiveBayes`

classifier that the software trained using the training set.

Label the test sample observations. Display the results for a random set of 10 observations in the test sample.

idx = randsample(sum(testIdx),10); label = predict(CMdl,XTest); table(YTest(idx),label(idx),'VariableNames',... {'TrueLabel','PredictedLabel'})

`ans=`*10×2 table*
TrueLabel PredictedLabel
______________ ______________
{'setosa' } {'setosa' }
{'versicolor'} {'versicolor'}
{'setosa' } {'setosa' }
{'virginica' } {'virginica' }
{'versicolor'} {'versicolor'}
{'setosa' } {'setosa' }
{'virginica' } {'virginica' }
{'virginica' } {'virginica' }
{'setosa' } {'setosa' }
{'setosa' } {'setosa' }

A goal of classification is to estimate posterior probabilities of new observations using a trained algorithm. Many applications train algorithms on large data sets, which can use resources that are better used elsewhere. This example shows how to efficiently estimate posterior probabilities of new observations using a Naive Bayes classifier.

Load Fisher's iris data set.

load fisheriris X = meas; % Predictors Y = species; % Response rng(1);

Partition the data set into two sets: one in the training set, and the other is new unobserved data. Reserve 10 observations for the new data set.

n = size(X,1); newInds = randsample(n,10); inds = ~ismember(1:n,newInds); XNew = X(newInds,:); YNew = Y(newInds);

Train a naive Bayes classifier. It is good practice to specify the class order. Assume that each predictor is conditionally, normally distributed given its label. Conserve memory by reducing the size of the trained SVM classifier.

Mdl = fitcnb(X(inds,:),Y(inds),... 'ClassNames',{'setosa','versicolor','virginica'}); CMdl = compact(Mdl); whos('Mdl','CMdl')

Name Size Bytes Class Attributes CMdl 1x1 5238 classreg.learning.classif.CompactClassificationNaiveBayes Mdl 1x1 12539 ClassificationNaiveBayes

The `CompactClassificationNaiveBayes`

classifier (`CMdl`

) uses less space than the `ClassificationNaiveBayes`

classifier (`Mdl`

) because the latter stores the data.

Predict the labels, posterior probabilities, and expected class misclassification costs. Since true labels are available, compare them with the predicted labels.

CMdl.ClassNames

`ans = `*3x1 cell*
{'setosa' }
{'versicolor'}
{'virginica' }

[labels,PostProbs,MisClassCost] = predict(CMdl,XNew); table(YNew,labels,PostProbs,'VariableNames',... {'TrueLabels','PredictedLabels',... 'PosteriorProbabilities'})

`ans=`*10×3 table*
TrueLabels PredictedLabels PosteriorProbabilities
______________ _______________ _________________________________________
{'setosa' } {'setosa' } 1 4.1259e-16 1.1846e-23
{'versicolor'} {'versicolor'} 1.0373e-60 0.99999 5.8053e-06
{'virginica' } {'virginica' } 4.8708e-211 0.00085645 0.99914
{'setosa' } {'setosa' } 1 1.4053e-19 2.2672e-26
{'versicolor'} {'versicolor'} 2.9308e-75 0.99987 0.00012869
{'setosa' } {'setosa' } 1 2.629e-18 4.4297e-25
{'versicolor'} {'versicolor'} 1.4238e-67 0.99999 9.733e-06
{'versicolor'} {'versicolor'} 2.0667e-110 0.94237 0.057625
{'setosa' } {'setosa' } 1 4.3779e-19 3.5139e-26
{'setosa' } {'setosa' } 1 1.1792e-17 2.2912e-24

MisClassCost

`MisClassCost = `*10×3*
0.0000 1.0000 1.0000
1.0000 0.0000 1.0000
1.0000 0.9991 0.0009
0.0000 1.0000 1.0000
1.0000 0.0001 0.9999
0.0000 1.0000 1.0000
1.0000 0.0000 1.0000
1.0000 0.0576 0.9424
0.0000 1.0000 1.0000
0.0000 1.0000 1.0000

`PostProbs`

and `MisClassCost`

are `15`

-by- `3`

numeric matrices, where each row corresponds to a new observation and each column corresponds to a class. The order of the columns corresponds to the order of `CMdl.ClassNames`

.

Load Fisher's iris data set. Train the classifier using the petal lengths and widths.

```
load fisheriris
X = meas(:,3:4);
Y = species;
```

Train a naive Bayes classifier. It is good practice to specify the class order. Assume that each predictor is conditionally, normally distributed given its label.

Mdl = fitcnb(X,Y,... 'ClassNames',{'setosa','versicolor','virginica'});

`Mdl`

is a `ClassificationNaiveBayes`

model. You can access its properties using dot notation.

Define a grid of values in the observed predictor space. Predict the posterior probabilities for each instance in the grid.

xMax = max(X); xMin = min(X); h = 0.01; [x1Grid,x2Grid] = meshgrid(xMin(1):h:xMax(1),xMin(2):h:xMax(2)); [~,PosteriorRegion] = predict(Mdl,[x1Grid(:),x2Grid(:)]);

Plot the posterior probability regions and the training data.

figure; % Plot posterior regions scatter(x1Grid(:),x2Grid(:),1,PosteriorRegion); % Adjust color bar options h = colorbar; h.Ticks = [0 0.5 1]; h.TickLabels = {'setosa','versicolor','virginica'}; h.YLabel.String = 'Posterior'; h.YLabel.Position = [-0.5 0.5 0]; % Adjust color map options d = 1e-2; cmap = zeros(201,3); cmap(1:101,1) = 1:-d:0; cmap(1:201,2) = [0:d:1 1-d:-d:0]; cmap(101:201,3) = 0:d:1; colormap(cmap); % Plot data hold on gh = gscatter(X(:,1),X(:,2),Y,'k','dx*'); title 'Iris Petal Measurements and Posterior Probabilities'; xlabel 'Petal length (cm)'; ylabel 'Petal width (cm)'; axis tight legend(gh,'Location','Best') hold off

A *misclassification cost* is
the relative severity of a classifier labeling an observation into
the wrong class.

There are two types of misclassification costs: true and expected.
Let *K* be the number of classes.

*True misclassification cost*— A*K*-by-*K*matrix, where element (*i*,*j*) indicates the misclassification cost of predicting an observation into class*j*if its true class is*i*. The software stores the misclassification cost in the property`Mdl.Cost`

, and used in computations. By default,`Mdl.Cost(i,j)`

= 1 if`i`

≠`j`

, and`Mdl.Cost(i,j)`

= 0 if`i`

=`j`

. In other words, the cost is`0`

for correct classification, and`1`

for any incorrect classification.*Expected misclassification cost*— A*K*-dimensional vector, where element*k*is the weighted average misclassification cost of classifying an observation into class*k*, weighted by the class posterior probabilities. In other words,$${c}_{k}={\displaystyle \sum _{j=1}^{K}\widehat{P}}\left(Y=j|{x}_{1},\mathrm{...},{x}_{P}\right)Cos{t}_{jk}.$$

the software classifies observations to the class corresponding with the lowest expected misclassification cost.

The *posterior probability* is
the probability that an observation belongs in a particular class,
given the data.

For naive Bayes, the posterior probability that a classification
is *k* for a given observation (*x*_{1},...,*x _{P}*)
is

$$\widehat{P}\left(Y=k|{x}_{1},\mathrm{..},{x}_{P}\right)=\frac{P\left({X}_{1},\mathrm{...},{X}_{P}|y=k\right)\pi \left(Y=k\right)}{P\left({X}_{1},\mathrm{...},{X}_{P}\right)},$$

where:

$$P\left({X}_{1},\mathrm{...},{X}_{P}|y=k\right)$$ is the conditional joint density of the predictors given they are in class

*k*.`Mdl.DistributionNames`

stores the distribution names of the predictors.*π*(*Y*=*k*) is the class prior probability distribution.`Mdl.Prior`

stores the prior distribution.$$P\left({X}_{1},\mathrm{..},{X}_{P}\right)$$ is the joint density of the predictors. The classes are discrete, so $$P({X}_{1},\mathrm{...},{X}_{P})={\displaystyle \sum _{k=1}^{K}P}({X}_{1},\mathrm{...},{X}_{P}|y=k)\pi (Y=k).$$

The *prior
probability* of a class is the believed relative frequency with which
observations from that class occur in a population.

[1] Hastie, T., R. Tibshirani, and J. Friedman. *The
Elements of Statistical Learning*, Second Edition. NY: Springer,
2008.

Calculate with arrays that have more rows than fit in memory.

This function fully supports tall arrays. You can use models trained on either in-memory or tall data with this function.

For more information, see Tall Arrays (MATLAB).

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

Use

`saveLearnerForCoder`

,`loadLearnerForCoder`

, and`codegen`

to generate code for the`predict`

function. Save a trained model by using`saveLearnerForCoder`

. Define an entry-point function that loads the saved model by using`loadLearnerForCoder`

and calls the`predict`

function. Then use`codegen`

to generate code for the entry-point function.This table contains notes about the arguments of

`predict`

. Arguments not included in this table are fully supported.Argument Notes and Limitations `Mdl`

For the usage notes and limitations of the model object, see Code Generation of the

`CompactClassificationNaiveBayes`

object.`X`

For general code generation,

`X`

must be a single-precision or double-precision matrix or a table containing`single`

or`double`

predictor variables.If you want to specify

`X`

as a table, then your model must be trained using a table, and you must ensure that your entry-point function for prediction:Accepts data as arrays

Creates a table from the data input arguments and specifies the variable names in the table

Passes the table to

`predict`

For an example of this table workflow, see Generate Code to Classify Numeric Data in Table. For more information on using tables in code generation, see Code Generation for Tables (MATLAB Coder) and Table Limitations for Code Generation (MATLAB Coder).

The number of rows, or observations, in

`X`

can be a variable size, but the number of columns in`X`

must be fixed.

For more information, see Introduction to Code Generation.

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