Here you’ll find everything about decision trees, whether it’s Hands-on tutorials on how to implement them with Python or conceptual articles.
A decision tree is a type of machine learning algorithm that is used for both regression and classification tasks. It is called a “decision tree” because it creates a tree-like model of decisions that it makes based on the input data. A decision tree works by splitting the input data into smaller and smaller subsets, based on the values of the input features. At each split, the algorithm chooses the feature and the value that produce the purest subset of data. This process continues until the subsets are pure enough, or until the tree reaches a predefined maximum depth. In Python, decision trees are easy to implement, since various Python packages include functions for decision trees: for example Scikit-Learn or Pytorch.
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