In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making.
As the name goes, it uses a tree-like model of decisions.
A decision tree is a flowchart like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) holds a class label.
Below are the important sections:
✔️Building a decision tree to predict the income of a given population, which is labelled as <= 50𝐾𝑎𝑛𝑑> 50K.
✔️The attributes (predictors) are age, working class type, marital status, gender, race etc.
✔️Data cleaning and Data modeling
✔️Building a decision tree with default hyperparameters
✔️Considering all the hyperparameters that can be tuned
✔️Chooseing the optimal hyperparameters using grid search cross-validation.
✔️Pruning of decision tree for considering relevant split/nodes.