Create a confusion matrix chart and sort the classes of the chart according to the class-wise true positive rate (recall) or the class-wise positive predictive value (precision).

Load and inspect thearrhythmiadata set.

loadarrhythmiaisLabels = unique(Y); nLabels = numel(isLabels)

nLabels = 13

tabulate(categorical(Y))

Value Count Percent 1 245 54.20% 2 44 9.73% 3 15 3.32% 4 15 3.32% 5 13 2.88% 6 25 5.53% 7 3 0.66% 8 2 0.44% 9 9 1.99% 10 50 11.06% 14 4 0.88% 15 5 1.11% 16 22 4.87%

The data contains 16 distinct labels that describe various degrees of arrhythmia, but the response (Y) includes only 13 distinct labels.

Train a classification tree and predict the resubstitution response of the tree.

Mdl = fitctree(X,Y); predictedY = resubPredict(Mdl);

Create a confusion matrix chart from the true labelsYand the predicted labelspredictedY. Specify'RowSummary'as'row-normalized'to display the true positive rates and false positive rates in the row summary. Also, specify'ColumnSummary'as'column-normalized'to display the positive predictive values and false discovery rates in the column summary.

fig = figure; cm = confusionchart(Y,predictedY,'RowSummary','row-normalized','ColumnSummary','column-normalized');

Resize the container of the confusion chart so percentages appear in the row summary.

fig_Position = fig.Position; fig_Position(3) = fig_Position(3)*1.5; fig.Position = fig_Position;

To sort the confusion matrix according to the true positive rate, normalize the cell values across each row by setting theNormalizationproperty to'row-normalized'and then usesortClasses. After sorting, reset theNormalizationproperty back to'absolute'to display the total number of observations in each cell.

cm.Normalization ='row-normalized'; sortClasses(cm,'descending-diagonal') cm.Normalization ='absolute';

To sort the confusion matrix according to the positive predictive value, normalize the cell values across each column by setting theNormalizationproperty to'column-normalized'and then usesortClasses. After sorting, reset theNormalizationproperty back to'absolute'to display the total number of observations in each cell.

cm.Normalization ='column-normalized'; sortClasses(cm,'descending-diagonal') cm.Normalization ='absolute';

Create a confusion matrix chart by using theconfusionchartfunction, and sort the classes to cluster similar classes by using the'cluster'option of thesortClasses函数。This example also shows how to cluster by using thepdist,linkage, andoptimalleaforderfunctions.

Generate a sample data set that contains eight distinct classes.

rng('default')% For reproducibilitytrueLabels = randi(8,1000,1); predictedLabels = trueLabels;

Insert confusion among classes {1,4,7}, {2,8}, and {5,6} for the first 200 samples.

rename = [4 8 3 7 6 5 1 2]; predictedLabels(1:100) = rename(predictedLabels(1:100)); rename = [7 8 3 1 6 5 4 2]; predictedLabels(101:200) = rename(predictedLabels(101:200));

Create a confusion matrix chart from the true labelstrueLabelsand the predicted labelspredictedLabels.

figure cm1 = confusionchart(trueLabels,predictedLabels);

sortClasses(cm1,'cluster')

m = confusionmat(trueLabels,predictedLabels); labels = [1 2 3 4 5 6 7 8];

D = pdist(m); idx = optimalleaforder(linkage(D),D); clusteredM = m(idx,idx); clusteredLabels = labels(idx);

cm2 = confusionchart(clusteredM,clusteredLabels); sortClasses(cm2,clusteredLabels)

Create a confusion matrix chart and sort the classes of the chart in a fixed order.

Load Fisher's iris data set.

loadfisheririsX = meas([51:150,1:50],:); Y = species([51:150,1:50],:);

Xis a numeric matrix that contains four petal measurements for 150 irises.Yis a cell array of character vectors that contains the corresponding iris species.

Train ak-nearest neighbor (KNN) classifier, where the number of nearest neighbors in the predictors (k) is 5. A good practice is to standardize numeric predictor data.

Mdl = fitcknn(X,Y,'NumNeighbors',5,'Standardize',1);

Predict the labels of the training data.

predictedY = resubPredict(Mdl);

Create a confusion matrix chart from the true labelsYand the predicted labelspredictedY.

cm = confusionchart(Y,predictedY);

By default,confusionchartsorts the classes into their natural order as defined bysort. In this example, the class labels are character vectors, soconfusionchartsorts the classes alphabetically. Reorder the classes of the confusion matrix chart in a fixed order.

sortClasses(cm,["versicolor","setosa","virginica"])