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

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 …

When can we use discriminant analysis?

Descriptive discriminant analysis is used when researchers want to assess the adequacy of classification, given the group memberships of the object under study. Predictive discriminant analysis is used when researchers want to assign objects to one of a number of known groups of objects.

Why is LDA better than logistic regression?

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.

Which model is better logistic regression or LDA?

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.

How do you explain logistic regression?

Logistic regression is a statistical model that uses Logistic function to model the conditional probability. This is read as the conditional probability of Y=1, given X or conditional probability of Y=0, given X. An example of logistic regression can be to find if a person will default their credit card payment or not.

How do you interpret Wilks Lambda discriminant analysis?

Wilks’ lambda is a measure of how well each function separates cases into groups. It is equal to the proportion of the total variance in the discriminant scores not explained by differences among the groups. Smaller values of Wilks’ lambda indicate greater discriminatory ability of the function.

Which is discriminant function analysis or logistic regression?

Posted on October 8, 2015 by Introspective-Mode in Discriminant Analysis, Key Statistical Techniques, Logistic Regression, Predicting Group Membership, Which Statistical Test? Discriminant Function Analysis (DFA) and Logistic Regression (LR) are so similar, yet so different. Which one when, or either at any time? Let’s see….

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”.

Which is the best test for discriminant function analysis?

Furthermore, it indicates which of the predictors are the most differentiating (highest discriminant weights), in other words, which dimensions distinguish best among these consumer segments and why respondents fall into one group versus another group. In summary, it is a technique for classification, differentiation, and profiling.

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.

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