Documentation

### This is machine translation

Mouseover text to see original. Click the button below to return to the English version of the page.

Note: This page has been translated by MathWorks. Click here to see
To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

## Visualize Decision Surfaces of Different Classifiers

This example shows how to plot the decision surface of different classification algorithms.

Load Fisher's iris data set.

```load fisheriris X = meas(:,1:2); y = categorical(species); labels = categories(y);```

`X` is a numeric matrix that contains two petal measurements for 150 irises. `Y` is a cell array of character vectors that contains the corresponding iris species.

Visualize the data using a scatter plot. Group the variables by iris species.

```gscatter(X(:,1),X(:,2),species,'rgb','osd'); xlabel('Sepal length'); ylabel('Sepal width');``` Train four different classifiers and store the models in a cell array.

`classifier_name = {'Naive Bayes','Discriminant Analysis','Classification Tree','Nearest Neighbor'};`

Train a naive Bayes model.

`classifier{1} = fitcnb(X,y);`

Train a discriminant analysis classifier.

`classifier{2} = fitcdiscr(X,y);`

Train a classification decision tree.

`classifier{3} = fitctree(X,y);`

Train a k-nearest neighbor classifier.

`classifier{4} = fitcknn(X,y);`

Create a grid of points spanning the entire space within some bounds of the actual data values.

```x1range = min(X(:,1)):.01:max(X(:,1)); x2range = min(X(:,2)):.01:max(X(:,2)); [xx1, xx2] = meshgrid(x1range,x2range); XGrid = [xx1(:) xx2(:)];```

Predict the iris species of each observation in `XGrid` using all classifiers. Plot the a scatter plot of the results.

```for i = 1:numel(classifier) predictedspecies = predict(classifier{i},XGrid); subplot(2,2,i); gscatter(xx1(:), xx2(:), predictedspecies,'rgb'); title(classifier_name{i}) legend off, axis tight end legend(labels,'Location',[0.35,0.01,0.35,0.05],'Orientation','Horizontal')``` Each classification algorithm generates different decision making rules. A decision surface can help you visualize these rules.

Download ebook