Decision trees are a popular machine learning algorithm that are used for both classification and regression tasks.
They are called "trees" because they have a hierarchical structure that resembles the branches of a tree.
A decision tree starts with a single node, which represents the entire dataset.
The algorithm then chooses the feature that best separates the dataset into two subsets.
This feature is used to create a new node in the tree, with one branch for each possible value of the feature.
The process is repeated recursively for each subset, until a stopping criterion is met
Decision trees are easy to interpret and visualize, as they can be represented graphically.
Popular libraries for building decision trees include scikit-learn, TensorFlow, and PyTorch.