Question

The purpose of pruning a decision tree is:
A. improving training set classification accuracy
B. improving generalization performance
C. dimensionality reduction
D. tree balancing

Answer 1

Answer:

dimensionality reduction

Explanation:

this will help the classifier to get the good accuracy

Answer 2

The purpose of pruning a decision tree is: dimensionality reduction

Explanation:

• Decision trees are like flow charts where there are internal nodes, root nodes and leaf nodes are present.
• The test that is to be carried out on an attribute will be represented by the internal node, the outcome of the test will be represented by the branch nodes and classes will be represented by leaf nodes.
• The size of the decision trees can be easily reduced with the help of the technique called pruning.
• Pruning refers to the removal of certain tree sections.
• These techniques help in the reduction of complexity and overfitting.
• There are two types of pruning such as pre pruning and post pruning.

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Pruning is a technique associated with Decision tree Linear regression Logistic regression SVM