Name:Kautuk Popli
Roll no:14023
Group A
• the assignment of objects of unknown class to existing classes.
Eg:-to determine what characteristics best discriminate between good and bad credit card customers.
When to use DA?
DA is useful in the following types of situations:
• Incomplete knowledge of future situations.
Eg:- a student applying to go to college may have to be classified as likely to succeed or likely to fail based on the characteristics of students who did succeed or fail in the past.
• The group can be identified but identification requires destroying the subject or plot.
Eg:- the strength of a rope can be measured by stressing it until it breaks but after the analysis is done, we can’t do anything with the information.
Discriminant analysis consists of two stages: in the first stage, the discriminant functions are derived; in the second stage, the discriminant functions are used to classify the cases.
While discriminant analysis does compute correlation measures to estimate the strength of the relationship, these correlations measure the relationship between the independent variables and the discriminant scores. A more useful measure to assess the utility of a discriminant model is classification accuracy, which compares predicted group membership based on the discriminant model to the actual, known group membership which is the value for the dependent variable.
Roll no:14023
Group A
Discriminant Analysis (DA)
The term discriminant analysis emerged from the research by Kendall & Stuart (1968) and Lachenbruch & Mickey (1968)
Discriminant analysis (both for discrimination and classification) is a statistical technique to organize and optimize:
• the description of differences among objects that belong to different groups or classes, and• the assignment of objects of unknown class to existing classes.
Eg:-to determine what characteristics best discriminate between good and bad credit card customers.
When to use DA?
DA is useful in the following types of situations:
• Incomplete knowledge of future situations.
Eg:- a student applying to go to college may have to be classified as likely to succeed or likely to fail based on the characteristics of students who did succeed or fail in the past.
• The group can be identified but identification requires destroying the subject or plot.
Eg:- the strength of a rope can be measured by stressing it until it breaks but after the analysis is done, we can’t do anything with the information.
While discriminant analysis does compute correlation measures to estimate the strength of the relationship, these correlations measure the relationship between the independent variables and the discriminant scores. A more useful measure to assess the utility of a discriminant model is classification accuracy, which compares predicted group membership based on the discriminant model to the actual, known group membership which is the value for the dependent variable.
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