Question

Consider a binary classification problem. Suppose I have trained a model on a linearly separable training set, and now I get a new labeled data point which is correctly classified by the model, and far away from the decision boundary. If I now add this new point to my earlier training set and re-train, in which cases is the learnt decision boundary likely to change?

A) When my model is a perceptron.
B) When my model is logistic regression.
C) When my model is an SVM.
D) When my model is Gaussian discriminant analysis.

Answer 1

Correct answer is (B) and (D)