The basic idea behind discriminant analysis is to find a linear combination of features that best separates the classes of data. Hereâs how it typically works in a toxicological context:
Data Collection: Gather a dataset of observations, including known class memberships and predictor variables. Model Building: Use the dataset to build a discriminant model that maximizes the distance between the means of different groups while minimizing the variance within each group. Validation: Test the model's accuracy using a separate validation set to ensure its predictive capability. Application: Apply the model to classify new observations or to understand the relationship between variables and group membership.
