# updateMetrics

Update performance metrics in ECOC incremental learning classification model given new data

*Since R2022a*

## Description

Given streaming data, `updateMetrics`

measures the performance of
a configured multiclass error-correcting output codes (ECOC) classification model for
incremental learning (`incrementalClassificationECOC`

object). `updateMetrics`

stores the
performance metrics in the output model.

`updateMetrics`

allows for flexible incremental learning. After you call
the function to update model performance metrics on an incoming chunk of data, you can perform
other actions before you train the model to the data. For example, you can decide whether you
need to train the model based on its performance on a chunk of data. Alternatively, you can
both update model performance metrics and train the model on the data as it arrives, in one
call, by using the `updateMetricsAndFit`

function.

To measure the model performance on a specified batch of data, call `loss`

instead.

returns an incremental learning model `Mdl`

= updateMetrics(`Mdl`

,`X`

,`Y`

)`Mdl`

, which is the input incremental learning model `Mdl`

modified to contain the model performance metrics on the incoming
predictor and response data, `X`

and `Y`

respectively.

When the input model is *warm* (`Mdl.IsWarm`

is
`true`

), `updateMetrics`

overwrites previously computed
metrics, stored in the `Metrics`

property, with the new values. Otherwise,
`updateMetrics`

stores `NaN`

values in
`Metrics`

instead.

## Examples

### Track Performance of Incremental Model

Train an ECOC classification model by using `fitcecoc`

, convert it to an incremental learner, and then track its performance to streaming data.

**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.

**Train ECOC Classification Model**

Fit an ECOC classification model to a random sample of half the data.

idxtt = randsample([true false],n,true); TTMdl = fitcecoc(X(idxtt,:),Y(idxtt))

TTMdl = ClassificationECOC ResponseName: 'Y' CategoricalPredictors: [] ClassNames: [1 2 3 4 5] ScoreTransform: 'none' BinaryLearners: {10x1 cell} CodingName: 'onevsone'

`TTMdl`

is a `ClassificationECOC`

model object representing a traditionally trained model.

**Convert Trained Model**

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

IncrementalMdl = incrementalLearner(TTMdl)

IncrementalMdl = incrementalClassificationECOC IsWarm: 1 Metrics: [1x2 table] ClassNames: [1 2 3 4 5] ScoreTransform: 'none' BinaryLearners: {10x1 cell} CodingName: 'onevsone' Decoding: 'lossweighted'

`IncrementalMdl`

is an `incrementalClassificationECOC`

model. The model display shows that the model is warm (`IsWarm`

is `1`

). Therefore, `updateMetrics`

can track model performance metrics given data.

**Track Performance Metrics**

Track the model performance on the rest of the data by using the `updateMetrics`

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

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`Metrics`

property. Note that the function does not fit the model to the chunk of data—the chunk is "new" data for the model.Store the classification error and first model coefficient of the first binary learner $${\beta}_{11}$$.

% Preallocation idxil = ~idxtt; nil = sum(idxil); numObsPerChunk = 50; nchunk = floor(nil/numObsPerChunk); mc = array2table(zeros(nchunk,2),VariableNames=["Cumulative","Window"]); beta11 = [IncrementalMdl.BinaryLearners{1}.Beta(1); zeros(nchunk,1)]; Xil = X(idxil,:); Yil = Y(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)); mc{j,:} = IncrementalMdl.Metrics{"ClassificationError",:}; beta11(j+1) = IncrementalMdl.BinaryLearners{1}.Beta(1); end

`IncrementalMdl`

is an `incrementalClassificationECOC`

model object that has tracked the model performance to observations in the data stream.

Plot a trace plot of the performance metrics and estimated coefficient $${\beta}_{11}$$ on separate tiles.

t = tiledlayout(2,1); nexttile plot(mc.Variables) xlim([0 nchunk]) ylabel("Classification Error") legend(mc.Properties.VariableNames) nexttile plot(beta11) ylabel("\beta_{11}") xlim([0 nchunk]); xlabel(t,"Iteration")

The cumulative loss is stable, whereas the window loss jumps throughout the training. $${\beta}_{11}$$ does not change because `updateMetrics`

does not fit the model to the data.

### Specify Orientation of Observations and Observation Weights

Train an ECOC classification model by using `fitcecoc`

, convert it to an incremental learner, track its performance on streaming data, and then fit the model to the data. For incremental learning functions, orient the observations in columns, and 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 from a stationary subject (`Y`

<= 2) has double the quality of the data from a moving subject. Create a weight variable that assigns a weight of 2 to observations from a stationary subject and 1 to a moving subject.

W = ones(n,1) + (Y <=2);

**Train ECOC Classification Model**

Fit an ECOC classification model to a random sample of half the data. Specify observation weights.

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

TTMdl = ClassificationECOC ResponseName: 'Y' CategoricalPredictors: [] ClassNames: [1 2 3 4 5] ScoreTransform: 'none' BinaryLearners: {10x1 cell} CodingName: 'onevsone'

`TTMdl`

is a `ClassificationECOC`

model object representing a traditionally trained ECOC classification model.

**Convert Trained Model**

Convert the traditionally trained model to a model for incremental learning.

IncrementalMdl = incrementalLearner(TTMdl)

IncrementalMdl = incrementalClassificationECOC IsWarm: 1 Metrics: [1x2 table] ClassNames: [1 2 3 4 5] ScoreTransform: 'none' BinaryLearners: {10x1 cell} CodingName: 'onevsone' Decoding: 'lossweighted'

`IncrementalMdl`

is an `incrementalClassificationECOC`

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

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

.

**Separately Track Performance Metrics and Fit Model**

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

and `fit`

functions. For incremental learning, orient the observations of the predictor data in columns. 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 that the observations are oriented in columns, and specify the observation weights.Store the classification error.

Call

`fit`

to fit the model to the incoming chunk of observations. Overwrite the previous incremental model to update the model parameters. Specify that the observations are oriented in columns, and specify the observation weights.

% 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),ObservationsIn="columns"); mc{j,:} = IncrementalMdl.Metrics{"ClassificationError",:}; IncrementalMdl = fit(IncrementalMdl,Xil(:,idx),Yil(idx), ... Weights=Wil(idx),ObservationsIn="columns"); end

`IncrementalMdl`

is an `incrementalClassificationECOC`

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.

plot(mc.Variables) xlim([0 nchunk]) legend(mc.Properties.VariableNames) ylabel("Classification Error") xlabel("Iteration")

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

### Perform Conditional Training

Incrementally train an ECOC 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 an ECOC classification model for incremental learning so that the maximum number of expected classes is 5, and the metrics window size is 1000. Prepare the model for `updateMetrics`

by fitting the model to the first 1000 observations.

Mdl = incrementalClassificationECOC(MaxNumClasses=5,MetricsWindowSize=1000); initobs = 1000; Mdl = fit(Mdl,X(1:initobs,:),Y(1:initobs));

`Mdl`

is an `incrementalClassificationECOC`

model object.

Determine whether the model is warm by querying the model property.

isWarm = Mdl.IsWarm

`isWarm = `*logical*
1

`Mdl.IsWarm`

is 1; therefore, `Mdl`

is warm.

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 first model coefficient of the first binary learner $${\beta}_{11}$$ to see how they evolve during training.

Track when

`fit`

trains the model.

% Preallocation numObsPerChunk = 100; nchunk = floor((n - initobs)/numObsPerChunk); beta11 = 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 beta11(j) = Mdl.BinaryLearners{1}.Beta(1); end

`Mdl`

is an `incrementalClassificationECOC`

model object trained on all the data in the stream.

To see how the model performance and $${\beta}_{11}$$ evolve during training, plot them on separate tiles.

t = tiledlayout(2,1); nexttile plot(beta11) hold on plot(find(trained),beta11(trained),"r.") xlim([0 nchunk]) ylabel("\beta_{11}") legend("\beta_{11}","Training occurs",Location="best") hold off nexttile plot(ce.Variables) yline(0.05,"--") xlim([0 nchunk]) ylabel("Misclassification Error Rate") legend(ce.Properties.VariableNames,Location="best") xlabel(t,"Iteration")

The trace plot of $${\beta}_{11}$$ shows periods of constant values, during which the loss within the previous observation window is at most 0.05.

### Configure Incremental Model to Track Performance Metrics for Model and Binary Learners

Prepare an incremental ECOC learner by specifying the maximum number of classes. Configure the model to track its performance and the performance of each binary learner in the model.

Create an ECOC model for incremental learning by calling `incrementalClassificationECOC`

. Specify a maximum of 5 expected classes in the data, and specify to update the performance metrics of binary learners in the model.

Mdl = incrementalClassificationECOC(MaxNumClasses=5,UpdateBinaryLearnerMetrics=true);

`Mdl`

is an `incrementalClassificationECOC`

model. All its properties are read-only.

Display the coding design matrix.

Mdl.CodingMatrix

`ans = `*5×10*
1 1 1 1 0 0 0 0 0 0
-1 0 0 0 1 1 1 0 0 0
0 -1 0 0 -1 0 0 1 1 0
0 0 -1 0 0 -1 0 -1 0 1
0 0 0 -1 0 0 -1 0 -1 -1

Each row corresponds to a class, and each column corresponds to a binary learner. For example, the first binary learner is for classes 1 and 2, and the fourth binary learner is for classes 1 and 5, where both learners assume class 1 as a positive class.

Determine whether the model is warm by querying the model property.

isWarm = Mdl.IsWarm

`isWarm = `*logical*
0

`Mdl.IsWarm`

is `0`

; therefore, `Mdl`

is not warm.

Determine the number of observations that incremental fitting functions, such as `fit`

, must process before measuring the performance of the model by displaying the size of the metrics warm-up period.

numObsBeforeMetrics = Mdl.MetricsWarmupPeriod

numObsBeforeMetrics = 1000

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);

The response vector `actid`

contains the activity IDs in integers: 1, 2, 3, 4, and 5 representing sitting, standing, walking, running, and dancing, respectively. For details on the data set, enter `Description`

at the command line.

Implement incremental learning by performing the following actions at each iteration:

Simulate a data stream by processing a chunk of 50 observations.

Measure model performance metrics on the incoming chunk using

`updateMetrics`

, and overwrite the input model.Fit the model to the incoming chunk, and overwrite the input model.

Store the first model coefficient of the first binary learner $${\beta}_{11}$$.

Store the misclassification error rates for the model and its binary learners.

% Preallocation numObsPerChunk = 50; nchunk = floor(n/numObsPerChunk); ce = array2table(zeros(nchunk,2),VariableNames=["Cumulative","Window"]); beta11 = zeros(nchunk,1); numBinaryLearners = length(Mdl.BinaryLearners); BinaryLearnerIsWarm = zeros(numBinaryLearners,1); numtrainobs = zeros(nchunk,numBinaryLearners); blMetrics = cell(numBinaryLearners,1); for k = 1:numBinaryLearners blMetrics{k} = array2table(zeros(nchunk,2),VariableNames=["Cumulative","Window"]); end % Incremental learning for j = 1:nchunk ibegin = min(n,numObsPerChunk*(j-1) + 1); iend = min(n,numObsPerChunk*j); idx = ibegin:iend; Mdl = updateMetrics(Mdl,X(idx,:),Y(idx)); ce{j,:} = Mdl.Metrics{"ClassificationError",:}; for k = 1:numBinaryLearners blMetrics{k}{j,:} = Mdl.BinaryLearners{k}.Metrics{"ClassificationError",:}; end Mdl = fit(Mdl,X(idx,:),Y(idx)); beta11(j) = Mdl.BinaryLearners{1}.Beta(1); for k = 1:numBinaryLearners numtrainobs(j,k) = Mdl.BinaryLearners{k}.NumTrainingObservations; if Mdl.BinaryLearners{k}.IsWarm == false BinaryLearnerIsWarm(k) = j; end end end

`Mdl`

is an `incrementalClassificationECOC`

model object trained on all the data in the stream.

To see how the performance metrics and $${\beta}_{11}$$ evolve during incremental learning, plot them on separate tiles.

figure t = tiledlayout(2,1); nexttile plot(beta11) ylabel("\beta_{11}") xlim([0 nchunk]) nexttile plot(ce.Variables) ylabel("ClassificationError") xline(numObsBeforeMetrics/numObsPerChunk,"--") xlim([0 nchunk]) legend(ce.Properties.VariableNames) xlabel(t,"Iteration")

mdlIsWarm = numObsBeforeMetrics/numObsPerChunk

mdlIsWarm = 20

The plot suggests that `fit`

always fits the model to the data, and `updateMetrics`

does not track the classification error until after the metrics warm-up period (20 chunks).

Plot the performance metrics of the binary learners for class 3.

bl = find(Mdl.CodingMatrix(3,:)); % Find binary learners for class 3 figure t = tiledlayout(length(bl),1); ax = zeros(length(bl),1); for i = 1 : length(bl) ax(i) = nexttile; plot(blMetrics{bl(i)}.Variables) xline(numObsBeforeMetrics/numObsPerChunk,"--") xline(BinaryLearnerIsWarm(1),":") xlim([0 nchunk]) positiveClass = find(Mdl.CodingMatrix(:,bl(i))==1); negativeClass = find(Mdl.CodingMatrix(:,bl(i))==-1); title(join(["Binary Learner for classes ",positiveClass," and ",negativeClass])) end legend(ax(1),blMetrics{bl(1)}.Properties.VariableNames,Location="best") linkaxes(ax) ylim([0,0.02]) ylabel(t,"ClassificationError") xlabel(t,"Iteration")

BinaryLearnerIsWarm(bl)

`ans = `*4×1*
42
41
51
60

A binary learner becomes warm after the software fits the learner to 1000 observations. Because each binary learner uses only the observations corresponding to its positive or negative classes, binary learners become warm at different learning iterations. Also, `updateMetrics`

updates the `Window`

metrics for the binary learners asynchronously.

The first plot shows that the classification error (or misclassification rate) is 0 for the binary learner that determines an activity between sitting (class 1) and walking (class 3). The next three plots show the misclassification rates for the three binary learners that distinguish walking (class 3) from standing (class 2), running (class 4), and dancing (class 5), respectively. These three binary learners have higher misclassification rates than the first binary learner.

## Input Arguments

`Mdl`

— Incremental learning model

`incrementalClassificationECOC`

model object

Incremental learning model whose performance is measured, specified as an `incrementalClassificationECOC`

model object. You can create
`Mdl`

by calling `incrementalClassificationECOC`

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

function.

If `Mdl.IsWarm`

is `false`

,
`updateMetrics`

does not track the performance of the model. Before
`updateMetrics`

can track performance metrics, you must perform both
of these actions:

Fit the input model

`Mdl`

to all expected classes (see the`MaxNumClasses`

and`ClassNames`

arguments of`incrementalClassificationECOC`

).Fit the input model

`Mdl`

to`Mdl.MetricsWarmupPeriod`

observations by passing`Mdl`

and the data to`fit`

. For more details, see Performance Metrics.

`X`

— Chunk of predictor data

floating-point matrix

Chunk of predictor data, specified as a floating-point matrix of *n*
observations and `Mdl.NumPredictors`

predictor
variables. The value of the `ObservationsIn`

name-value
argument determines the orientation of the variables and observations. The default
`ObservationsIn`

value is `"rows"`

, which indicates that
observations in the predictor data are oriented along the rows of
`X`

.

The length of the observation labels `Y`

and the number of observations in `X`

must be equal; `Y(`

is the label of observation * j*)

*j*(row or column) in

`X`

.**Note**

If

`Mdl.NumPredictors`

= 0,`updateMetrics`

infers the number of predictors from`X`

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

,`updateMetrics`

issues an error.`updateMetrics`

supports only floating-point input predictor data. If your input data includes categorical data, you must prepare an encoded version of the categorical data. Use`dummyvar`

to convert each categorical variable to a numeric matrix of dummy variables. Then, concatenate all dummy variable matrices and any other numeric predictors. For more details, see Dummy Variables.

**Data Types: **`single`

| `double`

`Y`

— Chunk of labels

categorical array | character array | string array | logical vector | floating-point vector | cell array of character vectors

Chunk of labels, specified as a categorical, character, or string array, a logical or floating-point vector, or a 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*)

*j*(row or column) in

`X`

.
`updateMetrics`

issues an error when one or both of these conditions
are met:

`Y`

contains a new label and the maximum number of classes has already been reached (see the`MaxNumClasses`

and`ClassNames`

arguments of`incrementalClassificationECOC`

).The

`ClassNames`

property of the input model`Mdl`

is nonempty, and the data types of`Y`

and`Mdl.ClassNames`

are different.

**Data Types: **`char`

| `string`

| `cell`

| `categorical`

| `logical`

| `single`

| `double`

**Note**

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

) value, `updateMetrics`

ignores the
observation. Consequently, `updateMetrics`

uses fewer than *n*
observations to compute the model performance, where *n* is the number of
observations in `X`

.

### Name-Value Arguments

Specify optional pairs of arguments as
`Name1=Value1,...,NameN=ValueN`

, where `Name`

is
the argument name and `Value`

is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.

**Example: **`ObservationsIn="columns",Weights=W`

specifies that the columns
of the predictor matrix correspond to observations, and the vector `W`

contains observation weights to apply during incremental learning.

`ObservationsIn`

— Predictor data observation dimension

`"rows"`

(default) | `"columns"`

Predictor data observation dimension, specified as `"rows"`

or
`"columns"`

.

**Example: **`ObservationsIn="columns"`

**Data Types: **`char`

| `string`

`Weights`

— Chunk of observation weights

floating-point vector of positive values

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

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.

**Example: **`Weights=W`

specifies the observation weights as the vector
`W`

.

**Data Types: **`double`

| `single`

## Output Arguments

`Mdl`

— Updated ECOC classification model for incremental learning

`incrementalClassificationECOC`

model object

Updated ECOC classification model for incremental learning, returned as an
incremental learning model object of the same data type as the input model
`Mdl`

, an `incrementalClassificationECOC`

object.

If the model is not warm, `updateMetrics`

does
not compute performance metrics. As a result, the `Metrics`

property of
`Mdl`

remains completely composed of `NaN`

values. If the
model is warm, `updateMetrics`

computes the cumulative and window performance
metrics on the new data `X`

and `Y`

, and overwrites the
corresponding elements of `Mdl.Metrics`

. All other properties of the input
model `Mdl`

carry over to the output model `Mdl`

. For more details, see
Performance Metrics.

## Tips

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 using`fit`

.

## Algorithms

### Performance Metrics

`updateMetrics`

and`updateMetricsAndFit`

track model performance metrics, specified by the row labels of the table in`Mdl.Metrics`

, from new data only when the incremental model is*warm*(`IsWarm`

property is`true`

).If you create an incremental model by using

`incrementalLearner`

and`MetricsWarmupPeriod`

is 0 (default for`incrementalLearner`

), the model is warm at creation.Otherwise, an incremental model becomes warm after the

`fit`

or`updateMetricsAndFit`

function performs both of these actions:Fit the incremental model to

`Mdl.MetricsWarmupPeriod`

observations, which is the*metrics warm-up period*.Fit the incremental model to all expected classes (see the

`MaxNumClasses`

and`ClassNames`

arguments of`incrementalClassificationECOC`

).

The

`Mdl.Metrics`

property stores two forms of each performance metric as variables (columns) of a table,`Cumulative`

and`Window`

, with individual metrics in rows. When the incremental model is warm,`updateMetrics`

and`updateMetricsAndFit`

update the metrics at the following frequencies:`Cumulative`

— The functions compute cumulative metrics since the start of model performance tracking. The functions update metrics every time you call the functions and base the calculation on the entire supplied data set.`Window`

— The functions compute metrics based on all observations within a window determined by the`Mdl.MetricsWindowSize`

property.`Mdl.MetricsWindowSize`

also determines the frequency at which the software updates`Window`

metrics. For example, if`Mdl.MetricsWindowSize`

is 20, the functions compute metrics based on the last 20 observations in the supplied data (`X((end – 20 + 1):end,:)`

and`Y((end – 20 + 1):end)`

).Incremental functions that track performance metrics within a window use the following process:

Store a buffer of length

`Mdl.MetricsWindowSize`

for each specified metric, and store a buffer of observation weights.Populate elements of the metrics buffer with the model performance based on batches of incoming observations, and store corresponding observation weights in the weights buffer.

When the buffer is filled, overwrite

`Mdl.Metrics.Window`

with the weighted average performance in the metrics window. If the buffer is overfilled when the function processes a batch of observations, the latest incoming`Mdl.MetricsWindowSize`

observations enter the buffer, and the earliest observations are removed from the buffer. For example, suppose`Mdl.MetricsWindowSize`

is 20, the metrics buffer has 10 values from a previously processed batch, and 15 values are incoming. To compose the length 20 window, the function uses the measurements from the 15 incoming observations and the latest 5 measurements from the previous batch.

The software omits an observation with a

`NaN`

score when computing the`Cumulative`

and`Window`

performance metric values.

### Observation Weights

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

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 `updateMetrics`

.

## Version History

**Introduced in R2022a**

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