1. Is there any derivation for the log loss function (from where did
    this function came)?

  2. How does gradient descent work in logistic regression?
    Like in linear regression our y was (y = b0 + b1x) and what we did
    there was randomly select a value for b1 and then kept updating it
    until we get a minimum MSE or the global minima.
    Here, in Logistic regression, our (y = sigmoid function), just wanted
    to know how gradient descent will work here? In linear regression we
    used a random value for b1, what will be that random value in logistic

  3. After reading tons of articles I also got to know that logistic
    regression uses MLE(Maximum likelihood estimation). I couldn’t figure
    out where exactly we use MLE in this algorithm

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