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Discriminant Analysis

Regularized linear and quadratic discriminant analysis

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 predict.


Classification LearnerTrain models to classify data using supervised machine learning


fitcdiscrFit discriminant analysis classifier
compactCompact discriminant analysis classifier
cvshrinkCross-validate regularization of linear discriminant
crossvalCross-validated discriminant analysis classifier
kfoldEdgeClassification edge for observations not used for training
kfoldLossClassification loss for observations not used for training
kfoldfunCross validate function
kfoldMarginClassification margins for observations not used for training
kfoldPredictPredict response for observations not used for training
lossClassification error
resubLossClassification error by resubstitution
logPLog unconditional probability density for discriminant analysis classifier
mahalMahalanobis distance to class means
nLinearCoeffsNumber of nonzero linear coefficients
compareHoldoutCompare accuracies of two classification models using new data
edgeClassification edge
marginClassification margins
resubEdgeClassification edge by resubstitution
resubMarginClassification margins by resubstitution
predictPredict labels using discriminant analysis classification model
resubPredictPredict resubstitution response of classifier


ClassificationDiscriminantDiscriminant analysis classification
CompactClassificationDiscriminantCompact discriminant analysis class
ClassificationPartitionedModelCross-validated classification model


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 a 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

Understand how predict classifies 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.

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