What is logistic regression?

Question

Accepted Answer

Logistic regression is a statistical method used to analyze and model the relationship between a binary dependent variable and one or more independent variables.

It is used to predict the probability of a binary outcome (0 or 1).
It is a type of regression analysis that uses a logistic function to model the relationship between the dependent and independent variables.
It is commonly used in machine learning and data analysis for classification problems.
Example: predicting whether a customer will buy a product or not based on their demographic and purchase history data.
Example: predicting whether a patient has a disease or not based on their symptoms and medical history data.

Accepted Answer

Logistic regression is a method used for binary classification problems (0/1, yes/No).

Logistic regression has a function form of a sigmoid function (prob(y) = 1/(1+e^-bx)). It fits the data to the above equation to get the parameters of the model using maximum likelihood estimation (MLE).

The cost function used in MLE is the log loss function

h = (-y*log(h(x)))-((1-y)*(1-log(h(x))))

Logistic regression can also be used for problems with multiple classes. It is called multinomial logistic regression

What is logistic regression?

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