When should I use logistic regression over linear discriminant analysis?

When should I use logistic regression over linear discriminant analysis?

While both are appropriate for the development of linear classification models, linear discriminant analysis makes more assumptions about the underlying data. Hence, it is assumed that logistic regression is the more flexible and more robust method in case of violations of these assumptions.

What is the main difference between linear discriminant analysis LDA and logistic regression?

Is my understanding right that, for a two class classification problem, LDA predicts two normal density functions (one for each class) that creates a linear boundary where they intersect, whereas logistic regression only predicts the log-odd function between the two classes, which creates a boundary but does not assume …

Why is logistic regression better than discriminant analysis?

In SPSS at least, logistic regression is easier to use than discriminant analysis when we have a mixture of numerical and categorical regressors, because it includes procedures for generating the necessary dummy variables automatically.

What is the difference between regression analysis and discriminant analysis?

The main difference between these two techniques is that regression analysis deals with a continuous dependent variable, while discriminant analysis must have a discrete dependent variable. The classification (factor) variable in the MANOVA becomes the dependent variable in discriminant analysis.

What are the assumptions of discriminant analysis?

The assumptions of discriminant analysis are the same as those for MANOVA. The analysis is quite sensitive to outliers and the size of the smallest group must be larger than the number of predictor variables. Multivariate normality: Independent variables are normal for each level of the grouping variable.

When to use discriminant analysis and logistic regression?

Logistic regression and discriminant analyses are both applied in order to predict the probability of a specific categorical outcome based upon several explanatory variables (predictors). The aim of this work is to evaluate the convergence of these two methods when they are applied in data from the health sciences.

What’s the difference between DFA and logistic regression?

LOGISTIC REGRESSION (LR): While logistic regression is very similar to discriminant function analysis, the primary question addressed by LR is “How likely is the case to belong to each group (DV)”. In contrast, the primary question addressed by DFA is “Which group (DV) is the case most likely to belong to”.

When to use linear regression or group regression?

If all variables are continuous, use linear regression – but we’ll then predict values rather than group membership unless you discretize the DV. If all variables are categorical, use log-linear analysis (which is an extension of the Chi-square).

Which is the goal of a logistic regression analysis?

As the focus is on probability (based on the probability theory), the goal of analyses is to create a linear combination of the log of the odds of a case being in one group or another. An odds ratio is estimated for each of the predictor variables in the model.