To interactively train a discriminant analysis model, use the Classification Learner app. For greater flexibility, train a discriminant analysis model using
fitcdiscr in 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|
|Cross-validated discriminant analysis classifier|
|Classification edge for observations not used for training|
|Classification loss for observations not used for training|
|Cross validate function|
|Classification margins for observations not used for training|
|Predict response for observations not used for training|
|Classification error by resubstitution|
|Log unconditional probability density for discriminant analysis classifier|
|Mahalanobis distance to class means|
|Number of nonzero linear coefficients|
|Compare accuracies of two classification models using new data|
|Classification edge by resubstitution|
|Classification margins by resubstitution|
Create and compare discriminant analysis classifiers, and export trained models to make predictions for new data.
Understand the steps for supervised learning and the characteristics of nonparametric classification and regression functions.
Categorical response data
Understand the discriminant analysis algorithm and how to fit a discriminant analysis model to data.
Understand the algorithm used to construct discriminant analysis classifiers.
Perform linear and quadratic classification of Fisher iris data.
Examine and improve discriminant analysis model performance.
Make a more robust and simpler model by removing predictors without compromising the predictive power of the model.
Discriminant analysis assumes that the data comes from a Gaussian mixture model. Understand how to examine this assumption.
observations using a discriminant analysis model.
This example shows how to visualize the decision surface for different classification algorithms.