In MATLAB, we can use the built-in fitctree()
function to create a decision tree classifier. Here's an example on how to do it:
main.m537 chars20 lines
In this example, we loaded the iris dataset and split it into training and testing sets using a cvpartition
object. Then, we trained a decision tree classifier using the training set and made predictions on the testing set. Finally, we evaluated the accuracy of the classifier by comparing the predicted labels with the true labels.
Note that this is a simple example and there are many ways to improve the performance of a decision tree classifier, such as pruning the tree or optimizing the hyperparameters.
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