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Train naive Bayes classification model for incremental learning

The `fit`

function fits a configured naive Bayes classification model for incremental learning (`incrementalClassificationNaiveBayes`

object) to streaming data. To additionally track performance metrics using the data as it arrives, use `updateMetricsAndFit`

instead.

To fit or cross-validate a naive Bayes classification model to an entire batch of data at once, see `fitcnb`

.

returns a naive Bayes classification model for incremental learning `Mdl`

= fit(`Mdl`

,`X`

,`Y`

)`Mdl`

, which represents the input naive Bayes classification model for incremental learning `Mdl`

trained using the predictor and response data, `X`

and `Y`

respectively. Specifically, `fit`

updates the conditional posterior distribution of the predictor variables given the data.

This example shows how to fit an incremental naive Bayes learner when you know only the expected maximum number of classes in the data.

Create a incremental naive Bayes model. Specify that the maximum number of expected classes is 5.

`Mdl = incrementalClassificationNaiveBayes('MaxNumClasses',5)`

Mdl = incrementalClassificationNaiveBayes IsWarm: 0 Metrics: [1×2 table] ClassNames: [1×0 double] ScoreTransform: 'none' DistributionNames: 'normal' DistributionParameters: {} Properties, Methods

`Mdl`

is an `incrementalClassificationNaiveBayes`

model. All its properties are read-only. `Mdl`

can encounter at most 5 unique classes. By default, the prior class distribution `Mdl.Prior`

is empirical, which means the software updates the prior distribution as it encounters labels.

`Mdl`

must be fit to data before you can use it to perform any other operations.

Load the human activity data set. Randomly shuffle the data.

load humanactivity n = numel(actid); rng(1) % For reproducibility idx = randsample(n,n); X = feat(idx,:); Y = actid(idx);

For details on the data set, enter `Description`

at the command line.

Fit the incremental model to the training data, in chunks of 50 observations at a time by using the `fit`

function. At each iteration:

Simulate a data stream by processing 50 observations.

Overwrite the previous incremental model with a new one fitted to the incoming observation.

Store the mean of the first predictor in the first class ${\mu}_{11}$ and the prior probability that the subject is moving (

`Y`

> 2) to see how they evolve during incremental training.

% Preallocation numObsPerChunk = 50; nchunk = floor(n/numObsPerChunk); mu11 = zeros(nchunk,1); priormoved = zeros(nchunk,1); % Incremental fitting for j = 1:nchunk ibegin = min(n,numObsPerChunk*(j-1) + 1); iend = min(n,numObsPerChunk*j); idx = ibegin:iend; Mdl = fit(Mdl,X(idx,:),Y(idx)); mu11(j) = Mdl.DistributionParameters{1,1}(1); priormoved(j) = sum(Mdl.Prior(Mdl.ClassNames > 2)); end

`IncrementalMdl`

is an `incrementalClassificationNaiveBayes`

model object trained on all the data in the stream.

To see how the parameters evolved during incremental learning, plot them on separate subplots.

figure; subplot(2,1,1) plot(mu11) ylabel('\mu_{11}') xlabel('Iteration') axis tight subplot(2,1,2) plot(priormoved); ylabel('Prior P(Subject Moved)') xlabel('Iteration') axis tight

`fit`

updates the posterior mean of the predictor distribution as it processes each chunk. Because the prior class distribution is empirical, $\pi $(subject is moving) changes as `fit`

processes each chunk.

This example shows how to fit an incremental naive Bayes learner when you know all the class names in the data.

Consider training a device to predict whether a subject is sitting, standing, walking, running, or dancing based on biometric data measured on the subject, and you know the class names map 1 through 5 to an activity. Also, suppose that the researchers plan to expose the device to each class uniformly.

Create an incremental naive Bayes learner for multiclass learning. Specify the class names and a uniform prior class distribution.

classnames = 1:5; Mdl = incrementalClassificationNaiveBayes('ClassNames',classnames,'Prior','uniform')

Mdl = incrementalClassificationNaiveBayes IsWarm: 0 Metrics: [1×2 table] ClassNames: [1 2 3 4 5] ScoreTransform: 'none' DistributionNames: 'normal' DistributionParameters: {5×0 cell} Properties, Methods

`Mdl`

is an `incrementalClassificationNaiveBayes`

model object. All its properties are read-only. During training, observed labels must be in `Mdl.ClassNames`

.

`Mdl`

must be fit to data before you can use it to perform any other operations.

Load the human activity data set. Randomly shuffle the data.

load humanactivity n = numel(actid); rng(1); % For reproducibility idx = randsample(n,n); X = feat(idx,:); Y = actid(idx);

For details on the data set, enter `Description`

at the command line.

Fit the incremental model to the training data by using the `fit`

function. Simulate a data stream by processing chunks of 50 observations at a time. At each iteration:

Process 50 observations.

Overwrite the previous incremental model with a new one fitted to the incoming observation.

Store the mean of the first predictor in the first class ${\mu}_{11}$ and the prior probability that the subject is moving (

`Y`

> 2) to see how they evolve during incremental training.

% Preallocation numObsPerChunk = 50; nchunk = floor(n/numObsPerChunk); mu11 = zeros(nchunk,1); priormoved = zeros(nchunk,1); % Incremental fitting for j = 1:nchunk ibegin = min(n,numObsPerChunk*(j-1) + 1); iend = min(n,numObsPerChunk*j); idx = ibegin:iend; Mdl = fit(Mdl,X(idx,:),Y(idx)); mu11(j) = Mdl.DistributionParameters{1,1}(1); priormoved(j) = sum(Mdl.Prior(Mdl.ClassNames > 2)); end

`IncrementalMdl`

is an `incrementalClassificationNaiveBayes`

model object trained on all the data in the stream.

To see how the parameters evolved during incremental learning, plot them on separate subplots.

figure; subplot(2,1,1) plot(mu11) ylabel('\mu_{11}') xlabel('Iteration') axis tight subplot(2,1,2) plot(priormoved); ylabel('Prior P(Subject Moved)') xlabel('Iteration') axis tight

`fit`

updates the posterior mean of the predictor distribution as it processes each chunk. Because the prior class distribution is specified as uniform, $\pi $(subject is moving) = 0.6 and does not change as `fit`

processes each chunk.

Train a naive Bayes classification model by using `fitcnb`

, convert it to an incremental learner, track its performance on streaming data, and then fit it to the data. Specify observation weights.

**Load and Preprocess Data**

Load the human activity data set. Randomly shuffle the data.

load humanactivity rng(1); % For reproducibility n = numel(actid); idx = randsample(n,n); X = feat(idx,:); Y = actid(idx);

For details on the data set, enter `Description`

at the command line.

Suppose that the data collected when the subject was not moving (`Y`

<= 2) has double the quality than when the subject was moving. Create a weight variable that attributes 2 to observations collected from a still subject, and 1 to a moving subject.

W = ones(n,1) + ~Y;

**Train Naive Bayes Classification Model**

Fit a naive Bayes classification model to a random sample of half the data.

```
idxtt = randsample([true false],n,true);
TTMdl = fitcnb(X(idxtt,:),Y(idxtt),'Weights',W(idxtt))
```

TTMdl = ClassificationNaiveBayes ResponseName: 'Y' CategoricalPredictors: [] ClassNames: [1 2 3 4 5] ScoreTransform: 'none' NumObservations: 12053 DistributionNames: {1×60 cell} DistributionParameters: {5×60 cell} Properties, Methods

`TTMdl`

is a `ClassificationNaiveBayes`

model object representing a traditionally trained naive Bayes classification model.

**Convert Trained Model**

Convert the traditionally trained model to a naive Bayes classification for incremental learning.

IncrementalMdl = incrementalLearner(TTMdl)

IncrementalMdl = incrementalClassificationNaiveBayes IsWarm: 1 Metrics: [1×2 table] ClassNames: [1 2 3 4 5] ScoreTransform: 'none' DistributionNames: {1×60 cell} DistributionParameters: {5×60 cell} Properties, Methods

`IncrementalMdl`

is an `incrementalClassificationNaiveBayes`

model. Because class names are specified in `Mdl.ClassNames`

, labels encountered during incremental learning must be in `Mdl.ClassNames`

.

**Separately Track Performance Metrics and Fit Model**

Perform incremental learning on the rest of the data by using the `updateMetrics`

and `fit`

functions. At each iteration:

Simulate a data stream by processing 50 observations at a time.

Call

`updateMetrics`

to update the cumulative and window classification error of the model given the incoming chunk of observations. Overwrite the previous incremental model to update the losses in the`Metrics`

property. Note that the function does not fit the model to the chunk of data—the chunk is "new" data for the model. Specify the observation weights.Call

`fit`

to fit the model to the incoming chunk of observations. Overwrite the previous incremental model to update the model parameters. Specify the observation weights.Store the minimal cost.

% Preallocation idxil = ~idxtt; nil = sum(idxil); numObsPerChunk = 50; nchunk = floor(nil/numObsPerChunk); mc = array2table(zeros(nchunk,2),'VariableNames',["Cumulative" "Window"]); Xil = X(idxil,:); Yil = Y(idxil); Wil = W(idxil); % Incremental fitting for j = 1:nchunk ibegin = min(nil,numObsPerChunk*(j-1) + 1); iend = min(nil,numObsPerChunk*j); idx = ibegin:iend; IncrementalMdl = updateMetrics(IncrementalMdl,Xil(idx,:),Yil(idx),... 'Weights',Wil(idx)); mc{j,:} = IncrementalMdl.Metrics{"MinimalCost",:}; IncrementalMdl = fit(IncrementalMdl,Xil(idx,:),Yil(idx),'Weights',Wil(idx)); end

`IncrementalMdl`

is an `incrementalClassificationNaiveBayes`

model object trained on all the data in the stream.

Alternatively, you can use `updateMetricsAndFit`

to update performance metrics of the model given a new chunk of data, and then fit the model to the data.

Plot a trace plot of the performance metrics.

h = plot(mc.Variables); xlim([0 nchunk]); ylabel('Minimal Cost') legend(h,mc.Properties.VariableNames) xlabel('Iteration')

The cumulative loss gradually stabilizes, whereas the window loss jumps.

Incrementally train a naive Bayes classification model only when its performance degrades.

Load the human activity data set. Randomly shuffle the data.

load humanactivity n = numel(actid); rng(1) % For reproducibility idx = randsample(n,n); X = feat(idx,:); Y = actid(idx);

For details on the data set, enter `Description`

at the command line.

Configure a naive Bayes classification model for incremental learning so that the maximum number of expected classes is 5, the tracked performance metric includes the misclassification error rate, and the metrics window size of 1000. Fit the configured model to the first 1000 observations.

Mdl = incrementalClassificationNaiveBayes('MaxNumClasses',5,'MetricsWindowSize',1000,... 'Metrics','classiferror'); initobs = 1000; Mdl = fit(Mdl,X(1:initobs,:),Y(1:initobs));

`Mdl`

is an `incrementalClassificationNaiveBayes`

model object.

Perform incremental learning, with conditional fitting, by following this procedure for each iteration:

Simulate a data stream by processing a chunk of 100 observations at a time.

Update the model performance on the incoming chunk of data.

Fit the model to the chunk of data only when the misclassification error rate is greater than 0.05.

When tracking performance and fitting, overwrite the previous incremental model.

Store the misclassification error rate and the mean of the first predictor in the second class ${\mu}_{21}$ to see how they evolve during training.

Track when

`fit`

trains the model.

% Preallocation numObsPerChunk = 100; nchunk = floor((n - initobs)/numObsPerChunk); mu21 = zeros(nchunk,1); ce = array2table(nan(nchunk,2),'VariableNames',["Cumulative" "Window"]); trained = false(nchunk,1); % Incremental fitting for j = 1:nchunk ibegin = min(n,numObsPerChunk*(j-1) + 1 + initobs); iend = min(n,numObsPerChunk*j + initobs); idx = ibegin:iend; Mdl = updateMetrics(Mdl,X(idx,:),Y(idx)); ce{j,:} = Mdl.Metrics{"ClassificationError",:}; if ce{j,2} > 0.05 Mdl = fit(Mdl,X(idx,:),Y(idx)); trained(j) = true; end mu21(j) = Mdl.DistributionParameters{2,1}(1); end

`Mdl`

is an `incrementalClassificationNaiveBayes`

model object trained on all the data in the stream.

To see how the model performance and ${\mu}_{21}$ evolved during training, plot them on separate subplots.

subplot(2,1,1) plot(mu21) hold on plot(find(trained),mu21(trained),'r.') ylabel('\mu_{21}') legend('\mu_{21}','Training occurs','Location','best') hold off subplot(2,1,2) plot(ce.Variables) ylabel('Misclassification Error Rate') xlabel('Iteration') legend(ce.Properties.VariableNames,'Location','best')

The trace plot of ${\mu}_{21}$ shows periods of constant values, during which the loss within the previous 1000 observation window is at most 0.05.

`Mdl`

— Naive Bayes classification model for incremental learning to fit to streaming data`incrementalClassificationNaiveBayes`

model objectNaive Bayes classification model for incremental learning to fit to streaming data, specified as an `incrementalClassificationNaiveBayes`

model object. You can create `Mdl`

directly or by converting a supported, traditionally trained machine learning model using the `incrementalLearner`

function. For more details, see the corresponding reference page.

`X`

— Chunk of predictor datafloating-point matrix

Chunk of predictor data to which the model is fit, specified as an *n*-by-`Mdl.NumPredictors`

floating point matrix.

The length of the observation labels `Y`

and the number of observations in `X`

must be equal; `Y(`

is the label of observation * j*)

`X`

.**Note**

If `Mdl.NumPredictors`

= 0, `fit`

infers the number of predictors from `X`

, and sets the congruent
property of the output model. Otherwise, if the number of predictor variables in the
streaming data changes from `Mdl.NumPredictors`

,
`fit`

issues an error.

**Data Types: **`single`

| `double`

`Y`

— Chunk of labelscategorical array | character array | string array | logical vector | floating-point vector | cell array of character vectors

Chunk of labels to which the model is fit, specified as a categorical, character, or string array, logical or floating-point vector, or cell array of character vectors.

The length of the observation labels `Y`

and the number of observations in `X`

must be equal; `Y(`

is the label of observation * j*)

`X`

. `fit`

issues an error when at least one of the conditions is met:`Y`

contains a newly encountered label and the maximum number of classes has been reached previously (see`MaxNumClasses`

and`ClassNames`

arguments of`incrementalClassificationNaiveBayes`

).The data types of

`Y`

and`Mdl.ClassNames`

are different.

**Data Types: **`char`

| `string`

| `cell`

| `categorical`

| `logical`

| `single`

| `double`

`Weights`

— Chunk of observation weightsfloating-point vector of positive values

Chunk of observation weights, specified as a floating-point vector of positive values. `fit`

weighs the observations in `X`

with the corresponding values in `Weights`

. The size of `Weights`

must equal *n*, which is the number of observations in `X`

.

By default, `Weights`

is `ones(`

.* n*,1)

For more details, including normalization schemes, see Observation Weights.

**Data Types: **`double`

| `single`

**Note**

If an observation (predictor or label) or weight contains at least one missing (`NaN`

) value, `fit`

ignores the observation. Consequently, `fit`

uses fewer than *n* observations to compute the model performance.

`Mdl`

— Updated naive Bayes classification model for incremental learning`incrementalClassificationNaiveBayes`

model objectUpdated naive Bayes classification model for incremental learning, returned as an
incremental learning model object of the same data type as the input model
`Mdl`

, an `incrementalClassificationNaiveBayes`

object.

In addition to updating distribution model parameters, `fit`

performs the following actions when `Y`

contains expected, but unprocessed, classes:

If you do not specify all expected classes by using the

`ClassNames`

name-value argument when you create the input model`Mdl`

using`incrementalClassificationNaiveBayes`

,`fit`

:Appends any newly encountered labels in

`Y`

to the tail of`Mdl.ClassNames`

.Expands

`Mdl.Cost`

to a*c*-by-*c*matrix, where*c*is the number of classes`Mdl.ClassNames`

. The resulting misclassification cost matrix is balanced.Expands

`Mdl.Prior`

to a length*c*vector of an updated empirical class distribution.

If you specify all expected classes when you create the input model

`Mdl`

or convert a traditionally trained naive Bayes model using`incrementalLearner`

, but you do not specify a misclassification cost matrix (`Mdl.Cost`

),`fit`

sets misclassification costs of processed classes to`1`

and unprocessed classes to`NaN`

. For example, if`fit`

processes the first two classes of a possible three classes,`Mdl.Cost`

is`[0 1 NaN; 1 0 NaN; 1 1 0]`

.

In the bag-of-tokens 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).

Unlike traditional training, incremental learning might not have a separate test (holdout) set. Therefore, to treat each incoming chunk of data as a test set, pass the incremental model and each incoming chunk to

`updateMetrics`

before training the model on the same data.

If predictor variable * j* has a conditional normal distribution (see the

`DistributionNames`

property), the software fits the distribution to the data by computing the class-specific weighted mean and the biased (maximum likelihood) estimate of the weighted standard deviation. For each class The weighted mean of predictor

*j*is$${\overline{x}}_{j|k}=\frac{{\displaystyle \sum _{\{i:{y}_{i}=k\}}{w}_{i}{x}_{ij}}}{{\displaystyle \sum _{\{i:{y}_{i}=k\}}{w}_{i}}},$$

where

*w*is the weight for observation_{i}*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$${s}_{j|k}={\left[\frac{{\displaystyle \sum _{\{i:{y}_{i}=k\}}{w}_{i}{\left({x}_{ij}-{\overline{x}}_{j|k}\right)}^{2}}}{{\displaystyle \sum _{\{i:{y}_{i}=k\}}{w}_{i}}}\right]}^{1/2}.$$

If all predictor variables compose a conditional multinomial distribution (see the
`DistributionNames`

property), the software fits the distribution
using the Bag-of-Tokens Model. The software stores the probability
that token * j* appears in class

`k`

`DistributionParameters{``k`

,`j`

}

.
With additive smoothing [1], the estimated probability is$$P(\text{token}j|\text{class}k)=\frac{1+{c}_{j|k}}{P+{c}_{k}},$$

where:

$${c}_{j|k}={n}_{k}\frac{{\displaystyle \sum _{\{i:{y}_{i}=k\}}^{}{x}_{ij}}{w}_{i}^{}}{{\displaystyle \sum _{\{i:{y}_{i}=k\}}^{}{w}_{i}}},$$ which is the weighted number of occurrences of token

*j*in class*k*.*n*is the number of observations in class_{k}*k*.$${w}_{i}^{}$$ is the weight for observation

*i*. The software normalizes weights within a class so that they sum to the prior probability for that class.$${c}_{k}={\displaystyle \sum _{j=1}^{P}{c}_{j|k}},$$ which is the total weighted number of occurrences of all tokens in class

*k*.

If predictor variable * j* has a conditional multivariate
multinomial distribution (see the

`DistributionNames`

property), the
software follows this procedure:The software collects a list of the unique levels, stores the sorted list in

`CategoricalLevels`

, and considers each level a bin. Each combination of predictor and class is a separate, independent multinomial random variable.For each class

*k*, the software counts instances of each categorical level using the list stored in`CategoricalLevels{`

.}`j`

The software stores the probability that predictor

in class`j`

has level`k`

*L*in the property`DistributionParameters{`

, for all levels in,`k`

}`j`

`CategoricalLevels{`

. With additive smoothing [1], the estimated probability is}`j`

$$P\left(\text{predictor}j=L|\text{class}k\right)=\frac{1+{m}_{j|k}(L)}{{m}_{j}+{m}_{k}},$$

where:

$${m}_{j|k}(L)={n}_{k}\frac{{\displaystyle \sum _{\{i:{y}_{i}=k\}}^{}I\{{x}_{ij}=L\}{w}_{i}^{}}}{{\displaystyle \sum _{\{i:{y}_{i}=k\}}^{}{w}_{i}^{}}},$$ which is the weighted number of observations for which predictor

*j*equals*L*in class*k*.*n*is the number of observations in class_{k}*k*.$$I\left\{{x}_{ij}=L\right\}=1$$ if

*x*=_{ij}*L*, and 0 otherwise.$${w}_{i}^{}$$ is the weight for observation

*i*. The software normalizes weights within a class so that they sum to the prior probability for that class.*m*is the number of distinct levels in predictor_{j}*j*.*m*is the weighted number of observations in class_{k}*k*.

For each conditional predictor distribution, `fit`

computes the weighted average and standard deviation.

If the prior class probability distribution is known (in other words, the prior distribution is not empirical), `fit`

normalizes observation weights to sum to the prior class probabilities in the respective classes. This action implies that the default observation weights are the respective prior class probabilities.

If the prior class probability distribution is empirical, the software normalizes the specified observation weights to sum to 1 each time you call `fit`

.

*Behavior changed in R2021b*

Starting in R2021b, naive Bayes incremental fitting functions `fit`

and `updateMetricsAndFit`

compute
biased (maximum likelihood) estimates of the weighted standard deviations for conditionally
normal predictor variables during training. In other words, for each class
*k*, incremental fitting functions normalize the sum of square weighted
deviations of the conditionally normal predictor
*x _{j}* by the sum of the weights in class

`fitcnb`

. The currently returned weighted standard deviation estimates differ
from those computed before R2021b by a factor of $$1-\frac{{\displaystyle \sum _{\{i:{y}_{i}=k\}}{w}_{i}{}^{2}}}{{\left({\displaystyle \sum _{\{i:{y}_{i}=k\}}{w}_{i}}\right)}^{2}}.$$

The factor approaches 1 as the sample size increases.

[1] Manning, Christopher D., Prabhakar Raghavan, and Hinrich Schütze. *Introduction to Information Retrieval*, NY: Cambridge University Press, 2008.

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