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

B) When my model is logistic regression.

D) When my model is Gaussian discriminant analysis.

Answer 2

The answer to this question is

Option (B) when my model is logistic regression. and

Option (D) When my model is Gaussian discriminant analysis.

• Predicting the output variable, which is discrete in two classes, is referred to as binary classification.

• The method of modeling the probability of a discrete result given an input variable is known as logistic regression.
• The most frequent logistic regression models include a binary result, which can accept two values such as true/false or yes/no.

• A learning algorithm based on a probabilistic assumption is Gaussian Discriminant Analysis.

SPJ3