Relationship between Linear and Logistics Regression

 

From linkedin post

In linear regression, our main aim is to estimate the values of Y-intercept and weights, minimize the cost function, and predict the output variable Y.

In logistic regression, we perform the exact same thing but with one small addition. We pass the result through a special function known as the Sigmoid Function to predict the output Y.

 

So, Logistic regression uses the same basic formula as linear regression but it is regressing for the probability of a categorical outcome.

Linear regression gives a continuous value of output y for a given input X. Whereas, logistic regression gives a continuous value of P(Y=1) for a given input X, which is later converted to Y=0 or Y=1 based on a threshold value.

That’s the reason, logistic regression has “Regression” in its name.

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