SVM(Support Vector Machine) quiz doubt

#1

Please explain Question number 1,6, and 10 ii) of the quiz … I have doubts in them.

#2

Hey @nikhil_sarda , you may find the following points useful to understand these questions better.

Question number 1 : By linear SVM in the question, the question means that the data which is completely linearly separable, meaning there are no outliers present, and so there is no need to introduce error term, and hence C =0 or better assume that c is not present.

Question number 6 : SVM tries to finds the “best” margin (distance between the line and the support vectors) that separates the classes and this reduces the risk of error on the data, while logistic regression does not, instead it can have different decision boundaries with different weights that are near the optimal point. Also the risk of overfitting is less in SVM, while Logistic regression is vulnerable to overfitting.

Question number 10 : In machine learning, a “kernel” is usually used to refer to the kernel trick, a method of using a linear classifier to solve a non-linear problem. It entails transforming linearly inseparable data like to linearly separable ones. The kernel function is what is applied on each data instance to map the original non-linear observations into a higher-dimensional space in which they become separable. For similarity point u can refer to link.

Hope this helps. Happy Learning :slight_smile:

#3

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