Wrapper model is ---- (O Feature selection Model (O Feature reduction model (O Feature counting model Q classification model The aim of feature reduction is ------ (O Reduce the number of feature O Reduce the diminution of data both class and feature (O Reduce the noise in data (O Nooneabove there isn't relation between Computer Vision with Machine learning Q True QO False Decision Tree (DT) is classifier algorithm predicts class of query based on (O nearest available cases highest information gained Q) Probabilty of nearest avaiable cases BothAandC Wrapper model isFeature selection ModelFeature reduction modelFeature counting modelclassification modelThe aim of feature reduction isReduce the number of featureReduce the diminution of data bothclass and featureReduce the noise in dataNo one abovethere isn't relation betweenComputer Vision with MachinelearningTrueFalseDecision Tree (DT) is classifieralgorithm predicts class of querybased onnearest available caseshighest information gainedprobability of nearest availablecasesBoth A and C

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Answered: Wrapper model is ---- (O Feature…

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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Wrapper model is ---- (O Feature selection Model (O Feature reduction model (O Feature counting model Q classification model The aim of feature reduction is ------ (O Reduce the number of feature O Reduce the diminution of data both class and feature (O Reduce the noise in data (O Nooneabove there isn't relation between Computer Vision with Machine learning Q True QO False Decision Tree (DT) is classifier algorithm predicts class of query based on (O nearest available cases highest information gained Q) Probabilty of nearest avaiable cases BothAandC

Wrapper model is
Feature selection Model
Feature reduction model
Feature counting model
classification model
The aim of feature reduction is
Reduce the number of feature
Reduce the diminution of data both
class and feature
Reduce the noise in data
No one above
there isn't relation between
Computer Vision with Machine
learning
True
False
Decision Tree (DT) is classifier
algorithm predicts class of query
based on
nearest available cases
highest information gained
probability of nearest available
cases
Both A and C

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