(Solved): pl shiws steps Logistic Regression for binary classification is a linear classifier. Mathematic ...

Expert Answer

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Let's start by considering the logistic regression model for binary classification. In logistic regression, we want to predict the probability of an instance belonging to a certain class, typically denoted as class 1. We can represent this probability as P(Y=1|X=xi,?), where Y is the label (0 or 1), X=xi represents the features of the instance, and ? is the parameter of the logistic regression model. To make a binary classification decision, we can define a threshold, typically 0.5. If P(Y=1|X=xi,?) is greater than or equal to the threshold, we predict the instance to be in class 1; otherwise, we predict it to be in class 0. Now, the probability of an instance belonging to class 0 can be calculated as P(Y=0|X=xi,?) = 1 - P(Y=1|X=xi,?). We subtract the probability of class 1 from 1 since we have only two classes. If we set a threshold of 0.5, we can rewrite the classification decision as follows: