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|
Create Discriminant Analysis Model
Improve Discriminant Analysis Model
Interpret Discriminant Analysis Model
|Cross-validated discriminant analysis classifier|
|Classification edge for cross-validated classification model|
|Classification loss for cross-validated classification model|
|Cross-validate function for classification|
|Classification margins for cross-validated classification model|
|Classify observations in cross-validated classification model|
|Classification error by resubstitution|
|Log unconditional probability density for discriminant analysis classifier|
|Mahalanobis distance to class means of discriminant analysis classifier|
|Number of nonzero linear coefficients|
|Compare accuracies of two classification models using new data|
|Classification edge by resubstitution|
|Classification margins by resubstitution|
|Compare accuracies of two classification models by repeated cross-validation|
- Train Discriminant Analysis Classifiers Using Classification Learner App
Create and compare discriminant analysis classifiers, and export trained models to make predictions for new data.
- Supervised Learning Workflow and Algorithms
Understand the steps for supervised learning and the characteristics of nonparametric classification and regression functions.
- Parametric Classification
Categorical response data
- Discriminant Analysis Classification
Understand the discriminant analysis algorithm and how to fit a discriminant analysis model to data.
- Creating Discriminant Analysis Model
Understand the algorithm used to construct discriminant analysis classifiers.
- Create and Visualize Discriminant Analysis Classifier
Perform linear and quadratic classification of Fisher iris data.
- Improving Discriminant Analysis Models
Examine and improve discriminant analysis model performance.
- Regularize Discriminant Analysis Classifier
Make a more robust and simpler model by removing predictors without compromising the predictive power of the model.
- Examine the Gaussian Mixture Assumption
Discriminant analysis assumes that the data comes from a Gaussian mixture model. Understand how to examine this assumption.
- Prediction Using Discriminant Analysis Models
predictclassifies observations using a discriminant analysis model.
- Visualize Decision Surfaces of Different Classifiers
This example shows how to visualize the decision surface for different classification algorithms.