Leo Breiman- as an Applied Statistician, he discovered tree-based methods of. Classification that later became machine learning. ○ Wrote CART: Classification . Breiman, L., J. Friedman, R. Olshen, and C. Stone, Classification and regression Breiman, Leo (). Leo Breiman. 1. Page 2. Outline. Regression Tree / Classification Tree . wm-greece.info Rnews_pdf. This paperback book describes a relatively new, com- puter based method for deriving a classification rule for assigning objects to groups. As the authors state .
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The next four paragraphs are from the book by Breiman et. al. for parametric and smoothing approaches is a blessing for regression trees. .. from: Random Forests by Leo Breiman and Adele Cutler. wm-greece.info∼adele/ forests. The monograph, “CART: Classification and Regression Trees,” Leo Breiman, Jerome Friedman, Richard Olshen, and Charles Stone (BFOS), repre-. Classification and Regression Trees reflects these two sides, Breiman, Leo; Friedman, Jerome H; Olshen, Richard A; Stone, Charles J.
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Export Cancel. References Breiman, L. The individual ergodic theorem of information theory. MR19,g Digital Object Identifier: Mathematical Reviews MathSciNet: You have access to this content.
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More like this. Regression Trees: where the target variable is continuous and tree is used to predict it's value. The CART algorithm is structured as a sequence of questions, the answers to which determine what the next question, if any should be.
The result of these questions is a tree like structure where the ends are terminal nodes at which point there are no more questions. A simple example of a decision tree is as follows [Source: Wikipedia]: The main elements of CART and any decision tree algorithm are: Rules for splitting data at a node based on the value of one variable; Stopping rules for deciding when a branch is terminal and can be split no more; and Finally, a prediction for the target variable in each terminal node.
The dataset consists of 5 variables and records as shown below: In this data set, "Class" is the target variable while the other four variables are independent variables.
To do this, we attach the CART node to the data set. Next, we choose our options in building out our tree as follows: On this screen, we pick the maximum tree depth, which is the most number of "levels" we want in the decision tree.
More about pruning in a different blog post.
On this screen, we choose stopping rules, which determine when further splitting of a node stops or when further splitting is not possible. In addition to maximum tree depth discussed above, stopping rules typically include reaching a certain minimum number of cases in a node, reaching a maximum number of nodes in the tree, etc. Conditions under which further splitting is impossible include when [Source: Handbook of Statistical Analysis and Data Mining Applications by Nisbet et al]: Only one case is left in a node; All other cases are duplicates of each other; and The node is pure all target values agree.