Inducing decision trees from examples involves the following steps:
Data collection: Collect a set of examples, each of which has a set of attributes (features) and a target variable (class label).
Attribute selection: Select an attribute as the root of the decision tree. The attribute should have the highest information gain or the highest gain ratio.
Splitting the data: Divide the training examples into subsets based on the selected attribute value.
Recursive tree building: Recursively apply the above steps to each subset of examples. Continue until all the examples in the subset belong to the same class or no more attributes are available for splitting.
Pruning the tree: Remove nodes that do not contribute significantly to the classification accuracy of the tree. This helps to reduce overfitting and improve the generalization performance of the tree.
Tree evaluation: Evaluate the performance of the decision tree on the test data set.
