Does logistic regression work with linearly separable data?

Does logistic regression work with linearly separable data?

Logistic Regression (LR) is a Generalized Linear Model (GLM). Although in spite of its name, the model is used for classification, not for regression. LR is a model used for only binary classification problems and it performs well on linearly separable classes.

What happens to maximum likelihood logistic regression if the data is linearly separable?

Here we show how maximum likelihood estimation for logistic regression can break down when training on linearly separable data. This means that maximum likelihood estimation will select parameter values of infinite magnitude, and will allow for many different possible parameter values.

What is meant by linearly separable?

In Euclidean geometry, linear separability is a property of two sets of points. These two sets are linearly separable if there exists at least one line in the plane with all of the blue points on one side of the line and all the red points on the other side.

When does logistic regression work better than other methods?

Logistic regression is easier to train and implement as compared to other methods. Logistic regression works well for cases where the dataset is linearly separable: A dataset is said to be linearly separable if it is possible to draw a straight line that can separate the two classes of data from each other.

Why does logistic regression not converge for a linearly separable data set?

Closed 3 years ago. Why does logistic regression not converge for a linearly separable data set? For linear separable data sets the model parameters go to infinity when mimizing the error function (according to Bishop2006, Pattern recognition and machine learning, section 4.3.2).

What happens when data is not linearly separable?

Once the data is transformed into the new higher dimension, the second step involves finding a linear separating hyperplane in the new space. The maximal marginal hyperplane found in the new space corresponds to a nonlinear separating hypersurface in the original space. Suppose the original feature space includes two variables X 1 and X 2.

What do you mean by binary outcome in logistic regression?

Logistic regression is a classification algorithm. It is used to predict a binary outcome based on a set of independent variables. Ok, so what does this mean? A binary outcome is one where there are only two possible scenarios—either the event happens (1) or it does not happen (0).