Table of Contents
Why logistic regression does not converge?
A frequent problem in estimating logistic regression models is a failure of the likelihood maximization algorithm to converge. In most cases, this failure is a consequence of data patterns known as complete or quasi-complete separation. For these patterns, the maximum likelihood estimates simply do not exist.
What does it mean when model does not converge?
Lack of convergence is an indication that the data do not fit the model well, because there are too many poorly fitting observations. A data set showing lack of convergence can usually be rescued by setting aside for separate study the person or item performances which contain these unexpected responses.
When should I not use logistic regression?
Logistic regression is easier to implement, interpret, and very efficient to train. If the number of observations is lesser than the number of features, Logistic Regression should not be used, otherwise, it may lead to overfitting. It makes no assumptions about distributions of classes in feature space.
Is logistic regression iterative?
The logistic regression uses an iterative maximum likelihood algorithm to fit the data.
When can we use logistic regression?
Logistic Regression is used when the dependent variable(target) is categorical. For example, To predict whether an email is spam (1) or (0) Whether the tumor is malignant (1) or not (0)
Why do logistic regression models fail to converge?
A frequent problem in estimating logistic regression models is a failure of the likelihood maximization algorithm to converge. In most cases, this failure is a consequence of data patterns known as complete or quasi-complete separation. For these patterns, the maximum likelihood estimates simply do not exist.
Is there a threshold to stop logistic regression?
For logistic regression, it is a little tricky since the goal is to maximize the likelihood. However, we still can set a reasonable threshold to stop the algorithm. Use the above simplest example.
What is random component in logistic regression?
Random Component: It refers a response variable (y), which need to satisfy some PDF assumption. For example: Linear regression of y (dependent variable) Logistic regression is not a linear model. We usually refer a linear regression to be a linear model or general linear model. Logistic regression is generalized linear model.
Why do logistic regression algorithms have multiple solutions?
Having multiple solutions or having infinity many solutions don’t mean that the algorithm can not converge as long as the algorithm can successfully reduce the error to reasonable low level and thus stop. For logistic regression, it is a little tricky since the goal is to maximize the likelihood.