Question

which of the following can be called a from discriminant analysis? a.) decision tree b.) clustering c.)svm d.) none of these​

Answer 1

❣Answer❣

D》None of these

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Answer 2

Answer:

The answer is none of these.

Explanation:

Decision tree is A multi-class linear discriminant is what it is. An instance is categorized into one of the K classes using a set of K linear discriminant functions. Making a tree-shaped diagram for a course of action or a statistical probability analysis is called decision tree analysis. It is applied to simplify difficult issues or branches. The decision tree's branches each represent potential outcomes.

In terms of contemporary statistical terminology, discriminant analysis is an example of supervised learning, while cluster analysis is an example of unsupervised learning. In most cases, even the number of groups into which the data should be categorized is unknown before the cluster analysis begins. Data is divided into groups whose identities are unknown in advance when cluster analysis is used. Contrasting with the situation for discrimination methods, which call for a training data set in which group memberships are known, this more constrained state of knowledge is the case.

By introducing the concept of (local) margin maximization into the conventional formulation of linear discriminant analysis (LDA), the SVM/LDA classifier can therefore be considered as a generalization of LDA. This classifier can be thought of as an improvement over the support vector machine (SVM) because it takes into account certain broad data-related information. By adding the concept of (local) margin maximization into the traditional LDA formulation, the SVM/LDA classifier can also be thought of as a generalization of linear discriminant analysis (LDA).

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