Tree pruning is performed in order to remove anomalies in the training data due to noise or outliers. The pruned trees are smaller and less complex.Tree Pruning ApproachesThere are two approaches to prune a tree −Pre-pruning − The tree is pruned by halting its construction early. Post-...
Tree pruning is described in Section 8.2.3. Scalability issues for the induction of decision trees from large databases are discussed in Section 8.2.4. Section 8.2.5 presents a visual mining approach to decision tree induction. 8.2.1 Decision Tree Induction During the late 1970s and early 1980...
rule inductionsequential covering approachstatistical pattern recognitionSummary Classification or decision trees lie at the intersection of the areas of statistical pattern recognition and machine learning. On one hand they are an example of a nonparametric approach that models the classification/regression ...
Decision tree pruning is the process of refining a decision tree model by removing unnecessary branches or nodes to prevent overfitting and improve its generalization ability on unseen data. AI generated definition based on: Advances in Computers, 2021 ...
3. Patel N, Upadhyay S. Study of various decision tree pruning methods with their empirical comparison in WEKA.Int J Comp Appl.60(12):20–25. [Google Scholar] 4. Berry MJA, Linoff G.Mastering Data Mining: The Art and Science of Customer Relationship Management.New York: John Wiley & ...
onunseendata.TreepruningisdescribedinSection7.3.2.Theextractionofclassificationrules fromdecisiontreesisdiscussedinSection7.3.3.Enhancementsofthebasicdecisiontree algorithmaregiveninSection7.3.4.Scalabilityissuesfortheinductionofdecisiontreesfrom largedatabasesarediscussedinSection7.3.5.Section7.3.6describestheintegratio...
Decisiontree决策树 Classification by Decision Tree Induction “What is a decision tree?”A decision tree is a flow-char-like tree structure ,where each internal node denotes a test on an attribute ,each branch represents an out-come of the test ,and leaf nodes represent classes or class distr...
induction (using a variety of splitting criteria) from distributed data. The resulting algorithms are provably exact in that the decision tree constructed from distributed data is identical to that obtained by the corresponding algo- rithm in the batch setting (i.e., when all of the data is av...
Semeraro, "A Further Compari- son of Simplification Methods for Decision-Tree Induction," Learning From Data: Artificial Intelligence and Statistics V, D. Fisher and H. Lenz, eds., Lecture Notes in Statistics. Berlin: Springer, no. 112, pp. 365-374, 1996....
This paper focuses on the variance introduced by the dis- cretization techniques used to handle continuous attributes in decision tree induction. Di erent discretization procedures are rst studied em- pirically, then means to reduce the discretization variance are proposed. The experiment shows that ...