To interactively train a discriminant analysis model, use the Classification Learner app. For
greater flexibility, train a discriminant analysis model using
the command-line interface. After training, predict labels or estimate
posterior probabilities by passing the model and predictor data to
|Classification Learner||Train models to classify data using supervised machine learning|
Learn how to train discriminant analysis classifiers.
While there are many Statistics and Machine Learning Toolbox™ algorithms for supervised learning, most use the same basic workflow for obtaining a predictor model.
Train a basic discriminant analysis classifier to classify irises in Fisher's iris data.
Perform linear and quadratic classification of Fisher iris data.
Examine and improve discriminant analysis classifier performance.
Make a more robust and simpler model by trying to remove predictors without hurting the predictive power of the model.
Discriminant analysis assumes that the data comes from a Gaussian mixture model. Learn how to examine this assumption.
Classification algorithms vary in speed, memory usage, interpretability, and flexibility.
Discriminant analysis is a classification method which assumes that different classes generate data based on different Gaussian distributions.
Understand the algorithm used to construct weighted classifiers.