Decision trees are a tree-based
machine
learning model used for classification and regression tasks
Decision trees
work
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
comparison
with more complex models.
Decision trees can
handle
both categorical and numerical data
Decision trees are prone to
overfitting
, 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
performance
Decision trees can be
used
for tasks such as prediction, product recommendation, etc.
Understanding binary Trees: A Beginner’s Guide