![]() The leaves in this bottom layer are the last step in a decision tree and represent the predicted outcome.Ī schematic overview of a simple decision tree Each split resembles an essential feature-specific question is a certain condition present or absent? Answering all those questions, until the bottom layer of the tree is reached, yields a prediction for the current sample. Essentially every node (including the root-node) splits the data set in subsets. A tree is composed out of the root-node, several tree-nodes and leaves. The main goal of understanding these intuitions is that by grasping the intuition, using and optimizing the algorithms in practice will become easier and will eventually yield better performance.Ī decision tree is a simple algorithm that essentially mimics a flowchart making them easy to interpret. Compared to more complex algorithms such as (deep) neural networks, random forests and gradient boosting are easy to implement, have relatively few parameters to tune and are less expensive regarding computational resources and (in general) require less extensive datasets. Tree-based algorithms have the advantage over linear or logistic regression in their ability to capture the nonlinear relationship that can be present in your data set. In this blog, I will first dive into one of the most basic algorithms (a decision tree) to be able to explain the intuition behind more powerful tree-based algorithms that use techniques to counter the disadvantages of these simple decision trees. For a data scientist, it is essential to understand the pros and cons of these predictive algorithms to select a well-suited one for the encountered problem. A deep dive into the mathematical intuition of these frequently used algorithm.īoth classification and regression problems can be solved by many different algorithms.
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