confusionmat - Compute confusion matrix for classification problem - MATLAB (original) (raw)

Compute confusion matrix for classification problem

Syntax

Description

[C](#mw%5Ff3ce64ac-9913-4cc9-bd2c-abcf1d46adf3) = confusionmat([group](#mw%5Fcc607721-b874-447d-96cd-b5b65e141c57),[grouphat](#mw%5F4ddf8995-0e10-4e71-99ed-cd1fab3ca698)) returns the confusion matrix C determined by the known and predicted groups in group and grouphat, respectively.

example

[C](#mw%5Ff3ce64ac-9913-4cc9-bd2c-abcf1d46adf3) = confusionmat([group](#mw%5Fcc607721-b874-447d-96cd-b5b65e141c57),[grouphat](#mw%5F4ddf8995-0e10-4e71-99ed-cd1fab3ca698),`'Order'`,[grouporder](#mw%5Ff7621fc9-0d5a-4373-ac75-d7ddd3ec3500)) uses grouporder to order the rows and columns of C.

[[C](#mw%5Ff3ce64ac-9913-4cc9-bd2c-abcf1d46adf3),[order](#mw%5F7b48ccef-b9ae-40d0-bb5d-b9a8c25d7921)] = confusionmat(___) also returns the order of the rows and columns of C in the variable order using any of the input arguments in previous syntaxes.

example

Examples

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Load a sample of predicted and true labels for a classification problem. trueLabels are the true labels for an image classification problem and predictedLabels are the predictions of a convolutional neural network.

load('Cifar10Labels.mat','trueLabels','predictedLabels');

Calculate the numeric confusion matrix. order is the order of the classes in the confusion matrix.

[m,order] = confusionmat(trueLabels,predictedLabels)

m = 10×10

923 4 21 8 4 1 5 5 23 6 5 972 2 0 0 0 0 1 5 15 26 2 892 30 13 8 17 5 4 3 12 4 32 826 24 48 30 12 5 7 5 1 28 24 898 13 14 14 2 1 7 2 28 111 18 801 13 17 0 3 5 0 16 27 3 4 943 1 1 0 9 1 14 13 22 17 3 915 2 4 37 10 4 4 0 1 2 1 931 10 20 39 3 3 0 0 2 1 9 923

order = 10×1 categorical airplane automobile bird cat deer dog frog horse ship truck

You can use confusionchart to plot the confusion matrix as a confusion matrix chart.

figure cm = confusionchart(m,order);

Figure contains an object of type ConfusionMatrixChart.

You do not need to calculate the confusion matrix first and then plot it. Instead, plot a confusion matrix chart directly from the true and predicted labels. You can also add column and row summaries and a title.

figure cm = confusionchart(trueLabels,predictedLabels, ... 'Title','My Title', ... 'RowSummary','row-normalized', ... 'ColumnSummary','column-normalized');

Figure contains an object of type ConfusionMatrixChart. The chart of type ConfusionMatrixChart has title My Title.

The ConfusionMatrixChart object stores the numeric confusion matrix in the NormalizedValues property and classes in the ClassLabels property.

ans = 10×10

ans = 10×1 categorical airplane automobile bird cat deer dog frog horse ship truck

Input Arguments

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Known groups for categorizing observations, specified as a numeric vector, logical vector, character array, string array, cell array of character vectors, or categorical vector.

group is a grouping variable of the same type as grouphat. The group argument must have the same number of observations as grouphat, as described in Grouping Variables (Statistics and Machine Learning Toolbox). The confusionmat function treats character arrays and string arrays as cell arrays of character vectors. Additionally, confusionmat treats NaN, empty, and 'undefined' values in group as missing values and does not count them as distinct groups or categories.

Example: {'Male','Female','Female','Male','Female'}

Predicted groups for categorizing observations, specified as a numeric vector, logical vector, character array, string array, cell array of character vectors, or categorical vector.

grouphat is a grouping variable of the same type as group. The grouphat argument must have the same number of observations as group, as described in Grouping Variables (Statistics and Machine Learning Toolbox). The confusionmat function treats character arrays and string arrays as cell arrays of character vectors. Additionally, confusionmat treats NaN, empty, and 'undefined' values in grouphat as missing values and does not count them as distinct groups or categories.

Example: [1 0 0 1 0]

Group order, specified as a numeric vector, logical vector, character array, string array, cell array of character vectors, or categorical vector.

grouporder is a grouping variable containing all the distinct elements in group and grouphat. Specify grouporder to define the order of the rows and columns of C. If grouporder contains elements that are not in group or grouphat, the corresponding entries in C are 0.

By default, the group order depends on the data type of s = [group;grouphat]:

For numeric vectors, the order is the sorted order of the unique values ins.
For logical vectors, the order isfalse (0) followed by true (1).
For categorical vectors, the order is the order returned by [categories](../../matlab/ref/categorical.categories.html)(s).
For other data types, the order is the order of first appearance in s.

Example: 'order',{'setosa','versicolor','virginica'}

Output Arguments

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Confusion matrix, returned as a square matrix with size equal to the total number of distinct elements in the group and grouphat arguments. C(i,j) is the count of observations known to be in group i but predicted to be in group j.

The rows and columns of C have identical ordering of the same group indices. By default, the group order depends on the data type of s = [group;grouphat]:

For numeric vectors, the order is the sorted order of the unique values ins.
For logical vectors, the order isfalse (0) followed by true (1).
For categorical vectors, the order is the order returned by [categories](../../matlab/ref/categorical.categories.html)(s).
For other data types, the order is the order of first appearance in s.

To change the order, specify grouporder,

The confusionmat function treats NaN, empty, and 'undefined' values in the grouping variables as missing values and does not include them in the rows and columns of C.

Order of rows and columns in C, returned as a numeric vector, logical vector, categorical vector, or cell array of character vectors. If group and grouphat are character arrays, string arrays, or cell arrays of character vectors, then the variable order is a cell array of character vectors. Otherwise, order is of the same type as group and grouphat.

Alternative Functionality

Use confusionchart to calculate and plot a confusion matrix. Additionally, confusionchart displays summary statistics about your data and sorts the classes of the confusion matrix according to the class-wise precision (positive predictive value), class-wise recall (true positive rate), or total number of correctly classified observations.

Version History

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When you create a confusion matrix using only one observation of typelogical, the confusionmat function returns a matrix instead of a scalar value. The default order of the rows and columns isfalse (0) followed by true (1).