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In 2011, authors of the Weka machine learning software described the C4.5 algorithm as "a landmark decision tree program that is probably the machine learning workhorse most widely used in practice to date". [2] It became quite popular after ranking #1 in the Top 10 Algorithms in Data Mining pre-eminent paper published by Springer LNCS in 2008. [3]
This process of top-down induction of decision trees (TDIDT) [5] is an example of a greedy algorithm, and it is by far the most common strategy for learning decision trees from data. [6] In data mining, decision trees can be described also as the combination of mathematical and computational techniques to aid the description, categorization and ...
The ID3 algorithm is used by training on a data set to produce a decision tree which is stored in memory. At runtime, this decision tree is used to classify new test cases (feature vectors) by traversing the decision tree using the features of the datum to arrive at a leaf node.
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 mining is a way of enhancing process models by analyzing the decision points in the model and finding the rules in those decision points based on data attributes. The rules for decision mining is extracted using decision tree algorithms, that analyses decision points to find out which properties of a case might lead to taking certain ...
An incremental decision tree algorithm is an online machine learning algorithm that outputs a decision tree. Many decision tree methods, such as C4.5, construct a tree using a complete dataset. Incremental decision tree methods allow an existing tree to be updated using only new individual data instances, without having to re-process past ...
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.