Ross Quinlan
In plain wordsAn Australian computer scientist who created ID3 and C4.5, influential programs that learn decision trees, flowchart-like rules for classifying examples.
Australian computer scientist who created the ID3 and C4.5 algorithms for learning decision treesdecision tree, n.: A model that makes a prediction by asking a series of yes-or-no questions about the input, like a flowchart, with each answer leading to the next question until it reaches a final answer.. Building on Earl Hunt's concept-learning systems and a chess-endgame problem posed by Donald MichieDonald Michie, prop. n.: A British codebreaker turned AI pioneer who built a game-playing learning machine out of matchboxes and founded Edinburgh's influential machine-intelligence research group., he developed ID3ID3, abbr.: An early, influential method for automatically building a decision tree from examples, choosing at each step the question that most reduces uncertainty about the answer. in the late 1970s; it builds a tree from the top down, choosing at each node the attribute with the greatest information gaininformation gain, n.: How much a question reduces uncertainty about the answer. A decision tree asks first the question that removes the most uncertainty, measured in bits.. Its successor C4.5C4.5, prop. n.: A widely used 1990s program that learns decision trees and rule sets from data; unlike its predecessor, it trims away unhelpful branches, copes with gaps in the data and can split on measured numbers., described in his book 'C4.5: Programs for Machine Learning' (1993), added handling of numeric attributes and missing values, pruning to reduce overfittingoverfitting, n.: When a model learns its training examples too well — quirks and noise included — so it does great on data it has seen but poorly on anything new, like a student who memorizes answers without understanding. and conversion of trees into rule sets, and was for years among the most widely used machine-learning programs; a 2008 survey placed it first among the top ten algorithms in data mining. He also developed FOIL (1990), an early inductive logic programminginductive logic programming, n.: A kind of machine learning that discovers general logical rules — such as 'X is a grandparent of Z if X is a parent of a parent of Z' — from examples and background knowledge. system for learning first-order rules, and commercialized the successor C5.0 through his company RuleQuest Research.
Related
- ID3ID3, abbr.: An early, influential method for automatically building a decision tree from examples, choosing at each step the question that most reduces uncertainty about the answer.
- C4.5C4.5, prop. n.: A widely used 1990s program that learns decision trees and rule sets from data; unlike its predecessor, it trims away unhelpful branches, copes with gaps in the data and can split on measured numbers.
- decision treedecision tree, n.: A model that makes a prediction by asking a series of yes-or-no questions about the input, like a flowchart, with each answer leading to the next question until it reaches a final answer.
- information gaininformation gain, n.: How much a question reduces uncertainty about the answer. A decision tree asks first the question that removes the most uncertainty, measured in bits.
- CARTCART, abbr.: A classic recipe for building decision trees that splits data two ways at every step, works for both categories and numbers, and then trims the tree back so it does not memorize noise.
- inductive logic programminginductive logic programming, n.: A kind of machine learning that discovers general logical rules — such as 'X is a grandparent of Z if X is a parent of a parent of Z' — from examples and background knowledge.
Sources
- Quinlan, J. R. (1986). Induction of Decision Trees. Machine Learning 1(1): 81–106.
- Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann.