In which case does the linear discriminant analysis fail?

But Linear Discriminant Analysis fails when the mean of the distributions are shared, as it becomes impossible for LDA to find a new axis that makes both the classes linearly separable. In such cases, we use non-linear discriminant analysis.

What are the assumptions of linear discriminant analysis?

Assumptions. 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.

What is the effect of the class prior on the decision boundary of linear discriminant analysis?

Linear and quadratic discriminant analysis Note the use of log-likelihood here. In another word, the discriminant function tells us how likely data x is from each class. The decision boundary separating any two classes, k and l, therefore, is the set of x where two discriminant functions have the same value.

Is LDA a classifier?

LDA as a classifier algorithm In the first approach, LDA will work as a classifier and posteriorly it will reduce the dimensionality of the dataset and a neural network will perform the classification task, the results of both approaches will be compared afterwards.

What do you report with linear discriminant analysis?

When to use Linear Discriminant Analysis?

• You want to use one variable in a prediction of another, or you want to quantify the numerical relationship between two variables.
• The variable you want to predict (your dependent variable) is categorical.
• Your dependent variables are all continuous.

Is Qda better than LDA?

LDA (Linear Discriminant Analysis) is used when a linear boundary is required between classifiers and QDA (Quadratic Discriminant Analysis) is used to find a non-linear boundary between classifiers. LDA and QDA work better when the response classes are separable and distribution of X=x for all class is normal.

How linear discriminant analysis is different from logistic regression?

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.