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How is a continuous variable used in a decision tree?
That means, as the decision variable is continuous type, you will use the metric (like Variance reduction) and chose the attribute which will give you the highest value of the chosen metric (i.e. variance reduction) for the threshold value of all attributes.
What are the two types of decision trees?
Decision trees can be divided into two types; categorical variable and continuous variable decision trees. There are two main types of decision trees that are based on the target variable, i.e., categorical variable decision trees and continuous variable decision trees. 1.
Why are decision trees less effective in making predictions?
In addition, decision trees are less effective in making predictions when the main goal is to predict the outcome of a continuous variable. This is because decision trees tend to lose information when categorizing variables into multiple categories. More Resources
Which is the best algorithm for continuous variable tree?
C4.5 algorithm solve this situation. In order to handle continuous attributes, C4.5 creates a threshold and then splits the list into those whose attribute value is above the threshold and those that are less than or equal to it. CART(classification and regression trees) algorithm solves this situation.
What is the difference between cart and decision tree?
CART is nonparametric and therefore does not rely on data belonging to a particular type of distribution. CART is not significantly impacted by outliers in the input variables. You can relax stopping rules to “overgrow” decision trees and then prune back the tree to the optimal size.
How is cart used in a regression tree?
CART can use the same variables more than once in different parts of the tree. This capability can uncover complex interdependencies between sets of variables. CART can be used in conjunction with other prediction methods to select the input set of variables.
How do decision tree learning algorithms deal with missing values?
There are several methods used by various decision trees. Simply ignoring the missing values (like ID3 and other old algorithms does) or treating the missing values as another category (in case of a nominal feature) are not real handling missing values. However those approaches were used in the early stages of decision tree development.
How is a continuous variable treated in C4.5?
C4.5 In order to handle continuous attributes, C4.5 creates a threshold and then splits the list into those whose attribute value is above the threshold and those that are less than or equal to it. CART (classification and regression trees) algorithm solves this situation.