Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Decision Trees in Variable Selection • In predictive modeling and data mining, we are often confronted with a large number of input variables in which some are irrelevant. • Decision tree is an alternative method for eliminating irrelevant variables and selecting variables which have predictive power. Decision Tree Results Differences in Calculation of Variable Importance Traditional Approach • Looks at the zero-order correlations between all possible inputs and the target Decision Tree Method • Sum of the worth statistics for an input across all the split nodes • Incorporates the effect of an input across various split • Captures a different dimension from a multiple regression Interactions • Interactions of input variables can also be observed from a decision tree map • One can construct an interaction term that combines input variables that can be useful in predictive modeling • Increases predictive power of the model Cross-Contributions of Decision Trees and Other Approaches Contribution of Other Methods to Decision Trees Association: • creates associations and sequence as composite inputs to decision tree to determine relationships Clustering : • might be useful in creating composite clusters for inclusion in decision trees Regression : • create linear composites for inclusion as inputs – a data reduction technique Neural Networks : • fit and fine –tune unclassified observations Contribution of Decision Trees To Other Methods Regression : • define strata for regression treatment • compute dummy variables • identify interactions • Impute missing values based on inputs with various levels of measurement Neural Networks : • Prequalify variables for inclusion, including bins for categories • Turn decision tree on predicted scores to assist in interpretation • Turn decision tree on score residuals Using Decision Tree Node for Variable Selection • The inputs which create significant splits in the development of the tree are passed to the next node with the role of Input. • Creates a special categorical variable called _NODE_ and optionally passes it to the next node as an input. • The variable _NODE_ can be used as a class input in the Regression node. Decision Tree Node Configurations •Variable Selection Property – YES •Leaf Variable Property – YES •Leaf Role Property - INPUT