It can help in predicting market trends and the impact of a new product on the market.Įven though discriminant analysis is similar to logistic regression, it is more stable than regression, especially when there are multiple classes involved. It helps you understand how each variable contributes towards the categorisation. You can use it to find out which independent variables have the most impact on the dependent variable. It has gained widespread popularity in areas from marketing to finance. Benefits of Discriminant Analysisĭiscriminant analysis is a valuable tool in statistics. Say what if you aren’t aware of the categories beforehand? In those cases, you would need to perform clustering.Īn example of discriminant analysis is using the performance indicators of a machine to predict whether it is in a good or a bad condition.
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This technique is utilised when you already know the output categories and want to come up with a method to successfully classify the dataset. If there are more than two groups, then it is called multiple discriminant analysis (MDA) or Canonical Varieties Analysis (CVA). If you are classifying the data into two groups, then it is known as Discriminant Function Analysis or DFA. These people are Fisher in the UK, Mahalanobis in India, and Hotelling in the US. Three people in three different countries are credited with giving birth to discriminant analysis. These equations are used to categorise the dependent variables.
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It takes continuous independent variables and develops a relationship or predictive equations. So, what is discriminant analysis and what makes it so useful?ĭiscriminant analysis, just as the name suggests, is a way to discriminate or classify the outcomes. Machine learning, pattern recognition, and statistics are some of the spheres where this practice is widely employed. Discriminant analysis is a vital statistical tool that is used by researchers worldwide.