Question

Using MLE, we find our logistic regression parameters by using the first-order derivative of the non-linear log-likelihood function, which has a closed-form solution.

a) Maximum Likelihood Estimation
b) Logistic Regression Estimation
c) Gradient Descent Method
d) Linear Regression Method

Answer 1

The subject of this question is Maximum Likelihood Estimation (MLE) in logistic regression. MLE involves using the first-order derivative of the non-linear log-likelihood function to find the optimal parameter values. This method is different from Gradient Descent Method and Linear Regression Method.

Step-by-step explanation:

The subject of this question is Maximum Likelihood Estimation (MLE). MLE is a statistical method used to estimate the parameters of a statistical model based on observed data. In logistic regression, MLE is used to find the parameters that maximize the likelihood of observing the given data.

The first-order derivative of the non-linear log-likelihood function is used in MLE to find the optimal parameter values. By setting the derivative equal to zero and solving for the parameters, we can obtain a closed-form solution.

This method is different from the Gradient Descent Method and Linear Regression Method, which are used in other types of regression models.