Decision trees are a tree-based
learning model used for classification and regression tasks
by making decisions based on feature values
Decision trees are easy to understand and to interpret, so they often used as a baseline model for
with more complex models.
Decision trees can
both categorical and numerical data
Decision trees are prone to
, which means that they can perform well on the training data but not on unseen data
Decision trees can also be used with other models, such as random forests, to improve their
Decision trees can be
for tasks such as prediction, product recommendation, etc.
Understanding binary Trees: A Beginner’s Guide