Bagging is generally used to help stabilize classifiers with unstable learning algorithms (optimal score search-ing algorithms). A classifier has a stable learning algorithm if, by changing the training data, the predictedclass labels in the test data don't change. For instance, the predictions of a decision tree might significantlychange with a small change in the training data. This definition depends on the amount of data, of course.Classifiers that are unstable with 10³ training examples may be stable with 10⁹ examples. Bagging worksby aggregating the answers of unstable classifiers trained over multiple training datasets. These multipledatasets are often not independent, generally sampled with replacement from the same training data.Boosting works by converting weak classifier (very simple models) to strong ones (models that can describecomplex relationships between the inputs and the class labels). A weak learner is a classifier whose outputof an test example attributes x; is only slightly correlated with its true class tį. That is, the weak learnerclassifies the data better than random, but not much better than random. In boosting, weak learners aretrained sequentially in a way that the current learner gives more emphasis to the examples that past learnsmade mistakes on.1.Suppose we decide to use a large deep feedforward network as a classifier with a small trainingdataset. Assume the network can perfectly fit the training data but we want to make sure it is accuratein our test data (without having access to the test data). Would you use boosting or bagging to helpimprove the classification accuracy? Describe what would be the problem of using the other approach.

Introduction: The ensemble approach known as "Bagging" is straightforward and extremely effective.…

assifier has a stable learning algorithm if, by changing the tr t data don't change. For instance, the predictions of a decisi change in the training data. This definition depends on the nstable with 103 training examples may be stable with 109 nswers of unstable classifiers trained over multiple training t independent, generally sampled with replacement from the nverting weak classifier (very simple models) to strong ones s between the inputs and the class labels). A weak learner i tributes x; is only slightly correlated with its true class t₁. tter than random, but not much better than random. In b

assifier has a stable learning algorithm if, by changing the tr t data don't change. For instance, the predictions of a decisi change in the training data. This definition depends on the nstable with 103 training examples may be stable with 109 nswers of unstable classifiers trained over multiple training t independent, generally sampled with replacement from the nverting weak classifier (very simple models) to strong ones s between the inputs and the class labels). A weak learner i tributes x; is only slightly correlated with its true class t₁. tter than random, but not much better than random. In b

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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