How do we set it up?
In simplified terms, the process of training a decision tree and
predicting the target features of query instances is as follows:
- Present a dataset containing of a number of training instances characterized by a number of descriptive features and a target feature
- Train the decision tree model by continuously splitting the target feature along the values of the descriptive features using a measure of information gain during the training process
- Grow the tree until we accomplish a stopping criteria create leaf nodes which represent the predictions we want to make for new query instances
- Show query instances to the tree and run down the tree until we arrive at leaf nodes
Then we are essentially done!