detectdrift

Detect drifts between baseline and target data using permutation testing

Syntax

DDiagnostics = detectdrift(Baseline,Target)

DDiagnostics = detectdrift(Baseline,Target,Name=Value)

Description

DDiagnostics = detectdrift(Baseline,Target) performs Permutation Testing to detect drift for each variable in the Baseline and Target data sets and returns the results in DDiagnostics.

DDiagnostics is a DriftDiagnostics object.

DDiagnostics = detectdrift(Baseline,Target,Name=Value) specifies additional options using one or more of the name-value arguments. For example, you can specify the metrics to use for the variables or the maximum number of permutations.

Examples

Compare Baseline and Target Data for Potential Drift

Generate baseline and target data with two variables, where the distribution parameters of the second variable change for target data.

rng('default') % For reproducibility
baseline = [normrnd(0,1,100,1),wblrnd(1.1,1,100,1)];
target = [normrnd(0,1,100,1),wblrnd(1.2,2,100,1)];

Compare the two data sets for any drift.

DDiagnostics = detectdrift(baseline,target)

DDiagnostics = 
  DriftDiagnostics

              VariableNames: ["x1"    "x2"]
       CategoricalVariables: []
                DriftStatus: ["Stable"    "Drift"]
                    PValues: [0.2850 0.0030]
        ConfidenceIntervals: [2x2 double]
    MultipleTestDriftStatus: "Drift"
             DriftThreshold: 0.0500
           WarningThreshold: 0.1000


  Properties, Methods

DDiagnostics is a DriftDiagnostics object. detectdrift displays some of the object properties.

You can further display the confidence intervals for the estimated p-values.

DDiagnostics.ConfidenceIntervals

ans = 2×2

    0.2572    0.0006
    0.3141    0.0087

The lower bound of the confidence interval for the estimated p-value for the first variable is greater than the warning threshold value of 0.1. Hence, detectdrift concludes the target data for the first variable is stable compared to the baseline data. The upper bound of the confidence interval for the estimated p-value for the second variable is smaller than the drift threshold of 0.05, so the drift status for this variable is drift. detectdrift detects the shift in the distribution parameters.

detectdrift uses the default Bonferroni method for testing multiple hypotheses. It first divides the warning and drift thresholds by the number of p-values, which in this case is two. Then it checks any of the p-values are still lower than any of the thresholds. You can see that the second p-value is still lower than the modified drift threshold, so the software sets the MultipleTestDriftStatus to drift for overall data.

Visualize the permutation results for both variables.

tiledlayout(1,2);
ax1 = nexttile;
plotPermutationResults(DDiagnostics,ax1,Variable="x1")
ax2 = nexttile;
plotPermutationResults(DDiagnostics,ax2,Variable="x2")

Bars to the right of the dashed line show the metric values that are greater than the threshold, which is the initial metric value detectdrift computes using the baseline and target data for each variable. The amount of the bars greater than the threshold is much more for variable x1, which indicates that there is not a significant drift between baseline and target data for that variable.

Specify Options for Batch Drift Detection

Load the sample data.

load humanactivity

For details on the data set, enter Description at the command line. Assign the first 250 observations as baseline data and next 250 as target data.

baseline = feat(1:250,:);
target = feat(251:500,:);

Test for drift on variables 5 to 10 using a warning threshold of 0.05 and a drift threshold of 0.01. All variables are continuous, so use the Kolmogorov-Smirnov metric for all variables. Use the False Discovery Rate method as the multiple test correction.

DDiagnostics = detectdrift(baseline(:,5:10),target(:,5:10),WarningThreshold=0.05, ...
        DriftThreshold=0.01,ContinuousMetric="ks",MultipleTestCorrection="fdr")

DDiagnostics = 
  DriftDiagnostics

              VariableNames: ["x1"    "x2"    "x3"    "x4"    "x5"    "x6"]
       CategoricalVariables: []
                DriftStatus: ["Drift"    "Drift"    "Drift"    ...    ]
                    PValues: [1.0000e-03 1.0000e-03 1.0000e-03 0.8810 ... ]
        ConfidenceIntervals: [2x6 double]
    MultipleTestDriftStatus: "Drift"
             DriftThreshold: 0.0100
           WarningThreshold: 0.0500


  Properties, Methods

Display the confidence intervals for p-value estimates.

DDiagnostics.ConfidenceIntervals

ans = 2×6

    0.0000    0.0000    0.0000    0.8593    0.0055    0.0000
    0.0056    0.0056    0.0056    0.9004    0.0196    0.0056

The lower confidence bound of the p-value for the 8th variable (corresponding variable name x4) is greater than the warning threshold, hence detectdrift decides the drift status for this variable is Stable. The upper confidence bound of the p-value for the 9th variable (corresponding variable name x5) is greater than the drift threshold, but lower than the warning threshold. Hence, detectdrift decides the drift status for this variable is Warning. Confidence intervals of all other variables are smaller than the drift threshold, so their drift statuses are Drift. Based on the False Discovery Rate method for multiple test correction, the software decides the drift status for overall data is Drift.

Visualize the p-values with the confidence intervals and corresponding drift status.

plotDriftStatus(DDiagnostics)

Specify Variables and Metrics to Use in Drift Detection

Load the data set NYCHousing2015.

load NYCHousing2015

The data set includes 10 variables with information on the sales of properties in New York City in 2015.

Remove outliers, convert the datetime array (SALEDATE) to the month numbers.

idx = isoutlier(NYCHousing2015.SALEPRICE);
NYCHousing2015(idx,:) = [];
NYCHousing2015.SALEDATE = month(NYCHousing2015.SALEDATE);

Define baseline and target data as information on the sales made in the months January and July, respectively.

tbl = NYCHousing2015;
baseline = tbl(tbl.SALEDATE==1,:);
target = tbl(tbl.SALEDATE==7,:);

Shuffle data.

n = numel(baseline(:,1));
rng(1); % For reproducibility
idx = randsample(n,n);
baseline = baseline(idx,:);
n = numel(target(:,1));
idx = randsample(n,n);
target = target(idx,:);

Test for potential drift between the baseline and target data. Specify the categorical variables and the metrics to use with each variable.

DDiagnostics = detectdrift(baseline(1:1500,:),target(1:1500,:), ...
    VariableNames=["BOROUGH","BUILDINGCLASSCATEGORY","LANDSQUAREFEET","GROSSSQUAREFEET","SALEPRICE"], ...
    CategoricalVariables=["BOROUGH","BUILDINGCLASSCATEGORY"], ...
    Metrics=["Hellinger","Hellinger","ad","ks","energy"])

DDiagnostics = 
  DriftDiagnostics

              VariableNames: ["BOROUGH"    "BUILDINGCLASSCATEGORY"    ...    ]
       CategoricalVariables: [1 2]
                DriftStatus: ["Drift"    "Stable"    "Drift"    ...    ]
                    PValues: [0.0260 0.1440 0.0070 0.0230 0.0110]
        ConfidenceIntervals: [2x5 double]
    MultipleTestDriftStatus: "Drift"
             DriftThreshold: 0.0500
           WarningThreshold: 0.1000


  Properties, Methods

detectdrift identifies drift between the baseline and target data for all variables, but BUILDINGCLASSCATEGORY.

Display the confidence intervals for the estimated p-values.

DDiagnostics.ConfidenceIntervals

ans = 2×5

    0.0171    0.1228    0.0028    0.0146    0.0055
    0.0379    0.1673    0.0144    0.0343    0.0196

Plot histogram for SALEPRICE.

plotHistogram(DDiagnostics,Variable="SALEPRICE")

Histogram shows the shift in the sale prices for the month of July compared to January.

Plot the empirical cumulative distribution function for baseline and target data of SALEPRICE.

plotEmpiricalCDF(DDiagnostics,Variable="SALEPRICE")

Plot the permutation results for SALEPRICE.

plotPermutationResults(DDiagnostics,Variable="SALEPRICE")

Compute Metrics without Estimating p-Values

Generate baseline and target data with three variables, where the distribution parameters of the second and third variables change for target data.

rng('default') % For reproducibility
baseline = [normrnd(0,1,100,1),wblrnd(1.1,1,100,1),betarnd(1,2,100,1)];
target = [normrnd(0,1,100,1),wblrnd(1.2,2,100,1),betarnd(1.7,2.8,100,1)];

Compute the initial metrics for all variables between the baseline and target data without estimating p-values.

DDiagnostics = detectdrift(baseline,target,EstimatePValues=false)

DDiagnostics = 
  DriftDiagnostics

           VariableNames: ["x1"    "x2"    "x3"]
    CategoricalVariables: []
                 Metrics: ["Wasserstein"    "Wasserstein"    "Wasserstein"]
            MetricValues: [0.2022 0.3468 0.0559]


  Properties, Methods

detectdrift only computes the initial metrics value for each variable using the baseline and target data. The properties associated with permutation testing and p-value estimation are either empty or contain NaNs.

summary(DDiagnostics)

          MetricValue       Metric    
          ___________    _____________

    x1      0.20215      "Wasserstein"
    x2      0.34676      "Wasserstein"
    x3     0.055922      "Wasserstein"

summary method only displays the metrics used and the initial metric value for each of the specified variables.

plotDriftStatus and plotPermutationResults do not produce plots and return warning messages. plotEmpiricalCDF and plotHistogram plot the ecdf and the histogram, respectively, for the first variable by default. They both return NaN for the p-value and drift status associated with the variable.

plotEmpiricalCDF(DDiagnostics)

plotHistogram(DDiagnostics)

Input Arguments

Baseline — Baseline data

numeric array | categorical array | table

Baseline data, specified as a numeric array, categorical array, or table. Baseline and Target data must have the same data type. When the input data is a categorical array, detectdrift treats each column as an independent categorical variable.

Data Types: single | double | categorical | table

Target — Target data

numeric array | categorical array | table

Target data, specified as a numeric array, categorical array, or table. Baseline and Target data must have the same data type. When the input data is a categorical array, detectdrift treats each column as an independent categorical variable.

Data Types: single | double | categorical | table

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: detectdrift(Baseline,Target,WarningThreshold=0.05,DriftThreshold=0.01,VariableNames=[''Weight'',''MPG''],ContinuousMetrics=''ad'') sets the warning threshold to 0.05 and drift threshold to 0.01, specifies Weight and MPG as the variables to test for drift detection, and Anderson-Darling as the metric to use in testing all continuous variables.

VariableNames — Variables to analyze for drift

string | array of unique strings | character vector | cell array of character vectors

Variables to analyze for drift, specified as a string, array of unique strings, character vector, or cell array of character vectors.

Example: VariableNames=["x1","x3"]

Data Types: string | char | cell

CategoricalVariables — List of categorical variables

"all" | string | array of unique strings | character vector | cell array of unique character vectors | vector of integer indices | vector of logical indices

List of categorical variables, specified as "all", a string, array of unique strings, character vector, cell array of unique character vectors, vector of integer indices, or vector of logical indices.

detectdrift treats the following as categorical variables: ordinal or nominal data types, or the categorical data type with the ordinal indicator set to true as categorical variables.

Example: CategoricalVariables="Zone"

Data Types: single | double | logical | string | cell

DriftThreshold — Threshold for detecting drift

0.05 (default) | scalar value from 0 to 1

Threshold for detecting drift, specified as a scalar value from 0 to 1.

detectdrift uses the drift threshold together with warning threshold to determine the drift status. The DriftThreshold value must be strictly lower than WarningThreshold value.

If the confidence interval for an estimated p-value is (Lower,Upper), then detectdrift determines the drift status as follows.

Drift Status	Condition
Drift	Upper < DriftThreshold
Warning	DriftThreshold < Lower < WarningThreshold or DriftThreshold < Upper < WarningThreshold
Stable	Lower > WarningThreshold

Example: DriftThreshold=0.01

Data Types: single | double

WarningThreshold — Threshold for potential drift warning

0.1 (default) | scalar value between 0 and 1

Threshold for potential drift warning, specified as a scalar value between 0 and 1.

detectdrift uses the warning threshold together with drift threshold to determine the drift status. WarningThreshold value must be strictly greater than DriftThreshold value.

If the confidence interval for an estimated p-value is (Lower,Upper), then detectdrift determines the drift status as follows.

Drift Status	Condition
Drift	Upper < DriftThreshold
Warning	DriftThreshold < Lower < WarningThreshold or DriftThreshold < Upper < WarningThreshold
Stable	Lower > WarningThreshold

Example: WarningThreshold=0.05

Data Types: single | double

MaxNumPermutations — Maximum number of permutations

1000 (default) | positive integer value

Maximum number of permutations, specified as a positive integer value. detectdrift increases the number of trials for permutation logarithmically according to a heuristic algorithm until it determines the drift status or reaches MaxNumPermutations. If detectdrift cannot determine the drift status by the end of the maximum number of permutations, then it sets the drift status to "Warning".

Example: MaxNumPermutations=1500

Data Types: single | double

Metrics — Metrics to use for each variable for detecting drift

string | string vector | character vector | cell array of character vectors | function handle | cell array of function handles | structure

Metrics used to detect drift for each variable, specified as one of the following:

• String, string vector, character vector, or cell array of character vectors representing one or more of the built-in metrics.

  Built-in metrics for continuous variables

  Value	Definition
  ''wasserstein''	Wasserstein
  ''energy''	Energy
  ''ks''	Kolmogorov-Smirnov
  ''ad''	Anderson-Darling

  Built-in metrics for categorical variables

  Value	Definition
  ''tv''	Total Variation
  ''psi''	Population Stability Index
  ''hellinger''	Hellinger
  ''chi2''	Chi-Square
  ''bhattacharyya''	Bhattarcharyya

• Function handle or a cell array of function handles. If you provide a function handle FUN as a metric, it is called as follows:

  FUN(BaselineVariable,TargetVariable),

  where BaselineVariable is the variable in Baseline and TargetVariable is the variable in Target. Output of FUN must be a scalar representing the metric value.

• Structure or a cell array of structures, where each structure has a single field and its value is a function handle. If you pass a structure, detectdrift uses the field name as the metric name. If the function handle is anonymous, detectdrift names it as 'CustomMetric_i', where i is the position in the Metrics value.

Metrics must have one value for each variable specified for drift detection using VariableNames and its size must be equal to the size of VariableNames.

If you specify metrics using Metrics, you cannot specify them using either ContinuousMetric or CategoricalMetric.

Example: Metrics=[''wasserstein'',''psi'',''hellinger'']

Data Types: string | cell | function_handle | struct

ContinuousMetric — Metric for drift detection in continuous variables

''wasserstein'' (default) | string | character vector | function handle | structure

Metric for drift detection in continuous variables, specified as one of the following:

• String or a character vector representing one or more of the built-in metrics.

  Built-in metrics for continuous variables

  Value	Definition
  ''wasserstein''	Wasserstein
  ''energy''	Energy
  ''ks''	Kolmogorov-Smirnov
  ''ad''	Anderson-Darling

• Function handle called as follows:

  FUN(BaselineVariable,TargetVariable),

  where BaselineVariable is the variable in Baseline and TargetVariable is the variable in Target. Output of FUN must be a scalar representing the metric value.

  If the function handle is not anonymous, detectdrift extracts the metric name from the provided function handle. If it is an anonymous function handle, then it names the metric 'CustomContinuousMetric'.

• Structure with a single field whose value is a function handle. In this case, detectdrift uses the field name as the metric name.

If you specify ContinuousMetric, then you cannot specify any other metrics using Metrics.

Example: ContinuousMetric=''ks''

Data Types: string | char | function_handle | struct

CategoricalMetric — Metric for drift detection in categorical variables

"hellinger" (default) | string | character vector | function handle | structure

Metric for drift detection in categorical variables, specified as one of the following.

• String or a character vector representing one or more of the built-in metrics.

  Built-in metrics for categorical variables

  Value	Definition
  ''tv''	Total Variation
  ''psi''	Population Stability Index
  ''hellinger''	Hellinger
  ''chi2''	Chi-Square
  ''bhattacharyya''	Bhattarcharyya

• Function handle called as follows:

  FUN(BaselineVariable,TargetVariable),

  where BaselineVariable is the variable in Baseline and TargetVariable is the variable in Target. Output of FUN must be a scalar representing the metric value.

  If the function handle is not anonymous, detectdrift extracts the metric name from the provided function handle. If it is an anonymous function handle, then it names the metric 'CustomCategoricalMetric'.

• Structure with a single field whose value is a function handle. In this case, detectdrift uses the field name as the metric name.

If you specify CategoricalMetric, then you cannot specify any other arguments using Metrics.

Example: CategoricalMetric="chi2"

Data Types: string | char | function_handle | struct

MultipleTestCorrection — Correction method for multiple hypothesis tests

"bonferroni" (default) | "fdr"

Correction method for multiple hypothesis tests, specified as one of the following.

• "bonferroni" - Bonferroni correction. If there are k variables specified for drift detection, detectdrift modifies the warning threshold and drift threshold by dividing them with k. Then, it checks if any of the p-values are smaller than the modified threshold values to decide the drift status.

• "fdr" - False discovery rate (FDR) method. detectdrift uses the Benjamini-Hochberg procedure to compute the false discovery rate. If there are k variables specified for drift detection, the method works in the following way:

  1. Ranks the p-values corresponding to these variables.

  2. Divides the ranks 1 to k by the number of variables k to obtain Q = [1/k, 2/k, 3/k ,…,k/k].

  3. Modifies the warning and drift thresholds for each sorted p-value by multiplying the initial warning and drift threshold values with the corresponding q value; for example, modified warning threshold for rank 3 is (WarningThreshold)*3/k.

  4. Checks if any of the sorted p-values is smaller than the corresponding modified warning or drift thresholds to decide on the drift status.

The multiple test correction methods provide a conservative estimate of the multivariable drift.

Example: MultipleTestCorrection="fdr"

UseParallel — Flag to run in parallel

false (default) | true

Flag to run in parallel, specified as true or false. If you specify UseParallel=true, the detectdrift function executes for-loop iterations in parallel by using parfor. This option requires Parallel Computing Toolbox™

Example: UseParallel=true

EstimatePValues — Indicator to estimate p-values

true (default) | false

Indicator to estimate the p-values during permutation testing, specified as either true or false. If you EstimatePValues as false, then detectdrift only computes the metrics.

Example: EstimatePValues=false

Output Arguments

DDiagnostics — Results of permutation testing for drift detection

DriftDiagnostics object

Results of permutation testing for drift detection, specified as a DriftDiagnostics object. detectdrift displays the following properties.

Property Name	Description
VariableNames	Variables analyzed for drift detection
CategoricalVariables	Indices of categorical variables in the data
DriftStatus	Drift status for each variable
PValues	Estimated p-value for each variable
ConfidenceIntervals	95% confidence interval bounds for the estimated p-values
MultipleTestDriftStatus	Drift status for the overall data
DriftThreshold	Threshold to determine the drift status
WarningThreshold	Threshold to determine the warning status

For a full list of the properties and their descriptions, see the DriftDiagnostics reference page.

Algorithms

Permutation Testing

detectdrift uses permutation testing to determine drift status for each variable in the Baseline data and its counterpart in the Target data. A permutation test is a nonparametric statistical significance test in which the distribution of a metric (test statistic) under the null hypothesis is obtained by computing the values of that metric under all possible rearrangements of a variable in Baseline and Target. Depending on the number of variables and observations, trying all possible permutations of a variable might be infeasible, hence detectdrift performs sufficiently many permutations to obtain a good estimate of the metric for the variable under consideration.

Under null hypothesis, i.e. no drift, many values of the metric recorded during permutation testing will be as extreme as or more extreme than the initial test statistic. If this is the case, it implies that with sufficiently high confidence, the observations of the specified variable in baseline data and in target data come from the same distribution. Hence no evidence of drift is found and detectdrift fails to reject the null hypothesis.

If the initial test statistic is identified as an outlier, then null hypothesis is rejected. This implies that with sufficiently high confidence, the observations of the specified variable in baseline data and in target data come from different distributions. Hence, drift is detected.

These are the steps that detectdrift takes in permutation testing:

• For a given variable with m observations in baseline data and n observations in target data, detectdrift computes an initial value of the metric from the original data.

• It then permutes the observations of the variable in baseline data and target data and separates them into two vectors with sizes m and n, respectively. Next, it computes the same metric value. detectdrift repeats this for MaxNumPermutations times to get a distribution of the specified metric.

• An estimate of the p-value is p = x/perm, where x is the number of times a metric value obtained from a permutation is greater than the value of the initial metric value, and perm is the number of permutations. With the Binomial distribution assumption for x, detectdrift estimates the 95% confidence interval for the p-value by using [~,CI] = binofit(x,perm,0.05).

Given the confidence intervals (Lower, Upper) of the p-values, detectdrift decides on the drift status based on the following conditions:

Drift Status	Condition
Drift	Upper < DriftThreshold
Warning	DriftThreshold < Lower < WarningThreshold or DriftThreshold < Upper < WarningThreshold
Stable	Lower > WarningThreshold

Metrics

detectdrift uses the following metrics as test statistics in permutation testing for detecting drift between baseline and target data.

Metrics for Continuous Variables

After defining $$E_b\left(x\right)$$ as the empirical cumulative distribution function (ecdf) of the baseline data over the common domain, $$E_t\left(x\right)$$ as the ecdf of the target data over the common domain, $$D\left(x\right)$$ as the joint ecdf of all data, and w as the difference between the edges of the bins, detectdrift computes the metrics for continuous variables as follows.

• Wasserstein

  $$W={\displaystyle \sum _{x}w*\left|E_b\left(x\right)-E_t\left(x\right)\right|}$$

• Energy

  $$En=\sqrt{2*{\displaystyle \sum _{x}w*{\left|E_b\left(x\right)-E_t\left(x\right)\right|}^2}}$$

• Kolmogorov-Smirnov

  $$KS=\max \left|E_b\left(x\right)-E_t\left(x\right)\right|$$

• Anderson-Darling

  $$AD={{\displaystyle \sum _x\left(\frac{\left|E_b\left(x\right)-E_t\left(x\right)\right|}{\sqrt{\left(m+n\right)D\left(x\right)*\left(1-D\left(x\right)\right)}}\right)}}^2$$

  m and n are the number of observations in baseline data and target data, respectively.

Metrics for Categorical Variables

After defining $$H_b\left(x\right)$$ as the percentage of the baseline data in the bins determined by combining the baseline and target data (jointly considering them across the same domain) and $$H_t\left(x\right)$$ as the percentage of the baseline data in the bins determined by combining the baseline and target data, detectdrift computes the metrics for categorical variables in the following way.

Note that, for categorical data, detectdrift adds 0.5 correction factor to histogram bin counts for each bin to handle empty bins (categories). This is equivalent to the assumption that the parameter p, probability that value of the variable would be in that category, has the prior distribution Beta(0.5,0.5), i.e. Jefferys prior assumption for the distribution parameter.

• Total Variation

  $$TV=0.5*{\displaystyle \sum _x\left|H_b\left(x\right)-H_t\left(x\right)\right|}$$

• Population Stability Index

  $$PSI=\max \left(0,{\displaystyle \sum _x\log \left(\frac{H_t\left(x\right)}{H_b\left(x\right)}\right)\left(H_t\left(x\right)-H_b\left(x\right)\right)}\right)$$

• Chi-Square

  $${\chi }^2={\displaystyle \sum _x\frac{{\left(H_t\left(x\right)-H_b\left(x\right)\right)}^2}{H_b\left(x\right)}}$$

• Bhattarcharyya

  $$B=\max \left(0,-\log \left(\min \left(1,{\displaystyle \sum _x\sqrt{H_b\left(x\right)*H_t\left(x\right)}}\right)\right)\right)$$

• Hellinger

  $$H=\max \left(0,\sqrt{1-\left(\min \left(1,{\displaystyle \sum _x\sqrt{H_b\left(x\right)*H_t\left(x\right)}}\right)\right)}\right)$$

References

[1] Benjamini, Yoav, and Yosef Hochberg. "Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing." Journal of the Royal Statistical Society, Series B (Methodological). Vol. 57, No. 1, pp. 289-300, 1995.

[2] Villani, Cédric. Topics in Optimal Transportation. Graduate Studies in Mathematics. Vol. 58, American Mathematical Society, 2000.

[3] Deza, Elena, and Michel Marie Deza. Encyclopedia of Distances, Springer Berlin Heidelberg, 2009.

Extended Capabilities

Automatic Parallel Support

Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.

To run in parallel, set the UseParallel name-value argument to true in the call to this function.

For more general information about parallel computing, see Run MATLAB Functions with Automatic Parallel Support (Parallel Computing Toolbox).

Version History

Introduced in R2022a