= to classify an object from the test data set which falls in this leaf de (NODE t). Predict this object as FP or NFP for the following es. OS(or c) = 0.30 O5(or c) = 2.5 ow all your work!

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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4. Suppose we have used a decision tree algorithm to build a classifi-
cation tree model for detection of Fault-Prone (FP) and Not Fault-
Prone (NFP) program modules or objects. We have encoded FP=0
and NFP=1. Also, assume that we are interested in a particular leaf
node (NODE t) in the tree model with mean value of 0.20. We would
like to classify an object from the test data set which falls in this leaf
node (NODE t). Predict this object as FP or NFP for the following
cases.
(a) (or c) = 0.30
(b) (or c) = 2.5
Show all your work!
Transcribed Image Text:4. Suppose we have used a decision tree algorithm to build a classifi- cation tree model for detection of Fault-Prone (FP) and Not Fault- Prone (NFP) program modules or objects. We have encoded FP=0 and NFP=1. Also, assume that we are interested in a particular leaf node (NODE t) in the tree model with mean value of 0.20. We would like to classify an object from the test data set which falls in this leaf node (NODE t). Predict this object as FP or NFP for the following cases. (a) (or c) = 0.30 (b) (or c) = 2.5 Show all your work!
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