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Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances.
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning.In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations.
Pruning is the practice of removing parameters (which may entail removing individual parameters, or parameters in groups such as by neurons) from an existing artificial neural networks. [1] The goal of this process is to maintain accuracy of the network while increasing its efficiency .
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. 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 ...
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 ...
[1] [2] When a decision tree is the weak learner, the resulting algorithm is called gradient-boosted trees; it usually outperforms random forest. [1] As with other boosting methods, a gradient-boosted trees model is built in stages, but it generalizes the other methods by allowing optimization of an arbitrary differentiable loss function .
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.
In computer science, a logistic model tree (LMT) is a classification model with an associated supervised training algorithm that combines logistic regression (LR) and decision tree learning. [1] [2] Logistic model trees are based on the earlier idea of a model tree: a decision tree that has linear regression models at its leaves to provide a ...