Leo Breiman
Encyclopedia
Leo Breiman was a distinguished statistician
at the University of California, Berkeley
. He was the recipient of numerous honors and awards, and was a member of the United States National Academy of Science.
Breiman's work helped to bridge the gap between statistics and computer science
, particularly in the field of machine learning
. His most important contributions were his work on classification and regression trees
and ensembles of trees fit to bootstrap samples. Bootstrap aggregation
was given the name bagging
by Breiman. Another of Breiman's ensemble approaches is the random forest
.
Statistician
A statistician is someone who works with theoretical or applied statistics. The profession exists in both the private and public sectors. The core of that work is to measure, interpret, and describe the world and human activity patterns within it...
at the University of California, Berkeley
University of California, Berkeley
The University of California, Berkeley , is a teaching and research university established in 1868 and located in Berkeley, California, USA...
. He was the recipient of numerous honors and awards, and was a member of the United States National Academy of Science.
Breiman's work helped to bridge the gap between statistics and computer science
Computer science
Computer science or computing science is the study of the theoretical foundations of information and computation and of practical techniques for their implementation and application in computer systems...
, particularly in the field of machine learning
Machine learning
Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases...
. His most important contributions were his work on classification and regression trees
Decision tree
A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm. Decision trees are commonly used in operations research, specifically...
and ensembles of trees fit to bootstrap samples. Bootstrap aggregation
Bootstrap aggregating
Bootstrap aggregating is a machine learning ensemble meta-algorithm to improve machine learning of statistical classification and regression models in terms of stability and classification accuracy. It also reduces variance and helps to avoid overfitting. Although it is usually applied to decision...
was given the name bagging
Bootstrap aggregating
Bootstrap aggregating is a machine learning ensemble meta-algorithm to improve machine learning of statistical classification and regression models in terms of stability and classification accuracy. It also reduces variance and helps to avoid overfitting. Although it is usually applied to decision...
by Breiman. Another of Breiman's ensemble approaches is the random forest
Random forest
Random forest is an ensemble classifier that consists of many decision trees and outputs the class that is the mode of the class's output by individual trees. The algorithm for inducing a random forest was developed by Leo Breiman and Adele Cutler, and "Random Forests" is their trademark...
.
External links
- Leo Breiman obituary, from the University of California, Berkeley
- Richard Olshen "A Conversation with Leo Breiman," Statistical Science Volume 16, Issue 2, 2001
- Leo Breiman from PORTRAITS OF STATISTICIANS
- A video record of a Leo Breiman's lecture about one of his machine learning techniques
