What are the advantages and disadvantages of using 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.

How is logistic regression different from 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.

Why is logistic regression better than LDA?

If the additional assumption made by LDA is appropriate, LDA tends to estimate the parameters more efficiently by using more information about the data. Because logistic regression relies on fewer assumptions, it seems to be more robust to the non-Gaussian type of data.

When should we use logistic regression?

When to use logistic regression. Logistic regression is applied to predict the categorical dependent variable. In other words, it’s used when the prediction is categorical, for example, yes or no, true or false, 0 or 1.

Why logistic regression is important?

It is used in statistical software to understand the relationship between the dependent variable and one or more independent variables by estimating probabilities using a logistic regression equation. This type of analysis can help you predict the likelihood of an event happening or a choice being made.

What is logistic discriminant analysis?

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 is the difference between logistic regression and LDA?

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 do we use logistic regression analysis?

Logistic regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary). Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables.