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=0and NFP=1. Also, assume that we are interested in a particular leafnode (NODE t) in the tree model with mean value of 0.20. We wouldlike to classify an object from the test data set which falls in this leafnode (NODE t). Predict this object as FP or NFP for the followingcases.(a) (or c) = 0.30(b) (or c) = 2.5Show all your work!

Given 4. Suppose we have used a decision tree algorithm to build a classifi-cation tree model for…

Answered: = to classify an object from the test…

= 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

Problem 1PE

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Concept explainers

A potential method of analysis

The complexity of the algorithm model emphasizes the potential method. This method is useful for examining a data structure's amortized time and space complexity.

Question

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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