If we tried to split data into parts, our first steps would be based on questions. The idea is quite simple and resembles the human mind. The picture above illustrates and explains decision trees by using exactly that, a decision tree diagram. By that time the algorithm had existed for 15 years. C4.5 addressed the shortcomings of its predecessor, ranking #1 in the Top 10 Algorithms in Data Mining pre-eminent paper (Springer LNCS, 2008). ID3 wasn’t ideal, so its author continued to upgrade the algorithm structure. It could cover other questions and propose some resulting nodes. Quinlan invented ID3 (Iterative Dichotomiser 3) using an impurity criterion called gain ratio. Important note: CART and all other decision tree classification algorithms only have two answers for each question (called binary trees). 1986: John Ross Quinlan proposed a new concept: trees with multiple answers.CART became a world-standard for decision tree analysis, and its development kept progressing. Main upgrades include truncating unnecessary trees, tunneling, and choosing the best tree version. Even today, CART is one of the most used methods for decision tree data analytics. It was a revolution in the world of algorithms. 1984: The official publication with a CART decision tree software.1977: Breiman, Stone, Friedman, and Olshen invented the first CART version.1974: Statistics professors Leo Breiman and Charles Stone from Berkeley and Jerome Friedman and Richard Olshen from Stanford started developing the classification and regression tree ( CART ) algorithm.It worked via splitting data to maximize the sum of cases in the modal category. 1972: The first classification tree appeared in the THAID project (by Messenger and Mandell).Exploring the human mind, researchers discovered the decision tree algorithm was useful for programming. In psychology, the decision tree methods were used to model the human concept of learning. 1966: The Institute of Computing Science in the Poznań University of Technology states that one of the first publications on the decision tree model was in 1966 (by Hunt).It had an impurity measure (we’ll get to that soon) and recursively split data into two subsets. 1963: The Department of Statistics at the University of Wisconsin–Madison writes that the first decision tree regression was invented in 1963 (AID project, Morgan and Sonquist). Here’s a quick look at decision tree history: Let’s take a deeper dive into decision tree analysis. Decision trees, while performing poorly in their basic form, are easy to understand and when stacked (Random Forest, XGBoost) reach excellent results. Yet, many algorithms can be quite difficult to understand, let alone explain. It only takes a few clicks to set and fit models in order to achieve solid results. In the world of machine learning, developers can create independent environments for projects easily. The Complete Guide to Decision Tree Analysis
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