Ivy_Accenture_Data_Analytics_using_R_Quiz-7

[True/False]: SVM is a supervised machine learning algorithm which can be used for both classification and regression.

• True

• False

What do you mean by generalization error in terms of the SVM?

• How far the hyper plane is from the support vectors

• How accurately the SVM can predict outcomes for unseen data

• The threshold amount of error in an SVM

The effectiveness of an SVM depends upon:

• Selection of kernel

• Kernel Parameters

• Soft Margin Parameter C

• All of the above

The cost parameter in the SVM means:

• The number of cross-validations to be made

• The kernel to be used

• The trade-off between misclassification and simplicity of the model

• None of the above

[True/False]: SVM is robust to outliers

• True

• False

The SVM’s are less effective when:

• The data is linearly separable

• The data is clean and ready to use

• The data is noisy and contains overlapping points

Which of the following are real world applications of the SVM?

• Text and Hypertext Categorization

• Image Classification

• Clustering of News Articles

• All of the above

Which of these is not a highlight of SVM?

• Ensures guaranteed optimality

• Suitable for both linearly and non-linearly separable data

• Works effectively for high dimensional datasets

• Over weight weak cases

What would happen when we use very large value of C

(C->infinity)?

• We can still classify data correctly for given setting of hyper parameter C

• We cannot classify data correctly for given setting of hyper parameter C

• Can’t Say

• None of these

[True/False]: Support vectors are the data points that lie closest to the decision surface.

• True

• False