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Pruning processes can be divided into two types (pre- and post-pruning). Pre-pruning procedures prevent a complete induction of the training set by replacing a stop criterion in the induction algorithm (e.g. max. Tree depth or information gain (Attr)> minGain). Pre-pruning methods are considered to be more efficient because they do not induce ...
C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. [1] C4.5 is an extension of Quinlan's earlier ID3 algorithm.The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier.
Bruce Ballard was the first to develop a technique, called *-minimax, that enables alpha-beta pruning in expectiminimax trees. [3] [4] The problem with integrating alpha-beta pruning into the expectiminimax algorithm is that the scores of a chance node's children may exceed the alpha or beta bound of its parent, even if the weighted value of each child does not.
Decision trees can also be seen as generative models of induction rules from empirical data. An optimal decision tree is then defined as a tree that accounts for most of the data, while minimizing the number of levels (or "questions"). [8] Several algorithms to generate such optimal trees have been devised, such as ID3/4/5, [9] CLS, ASSISTANT ...
Decision Tree Model. In computational complexity theory, the decision tree model is the model of computation in which an algorithm can be considered to be a decision tree, i.e. a sequence of queries or tests that are done adaptively, so the outcome of previous tests can influence the tests performed next.
As most tree based algorithms use linear splits, using an ensemble of a set of trees works better than using a single tree on data that has nonlinear properties (i.e. most real world distributions). Working well with non-linear data is a huge advantage because other data mining techniques such as single decision trees do not handle this as well.
The nodes and leaves can be identified from the given information and the decision trees are constructed. One such decision tree is as follows, Decision Tree branch for the information. Here the X-axis is represented as A and Y-axis as B. There are two cuts in the decision trees – nodes at 11 and 5 respective to A.
Alpha–beta pruning is a search algorithm that seeks to decrease the number of nodes that are evaluated by the minimax algorithm in its search tree. It is an adversarial search algorithm used commonly for machine playing of two-player combinatorial games ( Tic-tac-toe , Chess , Connect 4 , etc.).