Discriminant analysis is a standard statistical approach to classification. Here are the takeaways from the presentation of discriminant analysis on the Wine data:
- Discriminant analysis makes assumptions of multivariate normality within groups and homogeneity of covariance matrices across groups. You can use both the Discriminant procedure and IBM SPSS Statistics more generally to assess these assumptions.
- As the analyst, you must make decisions regarding prior probabilities, whether to classify based on pooled or separate covariance matrices and what dimensionality represents the data.
- The classification results table shows you overall classification accuracy and classification accuracy by class. You should assess accuracy not only on the training data, but also via leave-one-out analysis or cross-validation via the /SELECT subcommand.
- The standardized canonical discriminant...
