Decision Tree Learning

Decision Tree Learning is a type of supervised machine learning algorithm that can be used to construct a prediction model from a data set. It works by mapping out possibilities and outcomes using a tree-like structure, in which each branch is based on a decision and each leaf is an outcome. By using what is known as a splitting criteria, the decision tree is able to determine which class a data point belongs to. It can be used for both classification and regression problems, depending on the dataset and desired outcome. Decision Tree Learning is widely used in fields like data mining, statistics and predictive analytics, as it is an effective and efficient way to build models from data.