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.

Apps

Classification Learner

Train models to classify data using supervised machine learning

Functions

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Create Discriminant Analysis Model

`fitcdiscr`	Fit discriminant analysis classifier
`makecdiscr`	Construct discriminant analysis classifier from parameters
`compact`	Compact discriminant analysis classifier

Improve Discriminant Analysis Model

`cvshrink`	Cross-validate regularization of linear discriminant
`partialDependence`	Compute partial dependence
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots

Cross-Validate

`crossval`	Cross-validated discriminant analysis classifier
`kfoldEdge`	Classification edge for observations not used for training
`kfoldLoss`	Classification loss for observations not used for training
`kfoldfun`	Cross validate function
`kfoldMargin`	Classification margins for observations not used for training
`kfoldPredict`	Predict response for observations not used for training

Measure Performance

`loss`	Classification error
`resubLoss`	Classification error by resubstitution
`logp`	Log unconditional probability density for discriminant analysis classifier
`mahal`	Mahalanobis distance to class means
`nLinearCoeffs`	Number of nonzero linear coefficients
`compareHoldout`	Compare accuracies of two classification models using new data
`edge`	Classification edge
`margin`	Classification margins
`resubEdge`	Classification edge by resubstitution
`resubMargin`	Classification margins by resubstitution

Classify Observations

`predict`	Predict labels using discriminant analysis classification model
`resubPredict`	Predict resubstitution labels of discriminant analysis classification model
`classify`	Discriminant analysis

Classes

`ClassificationDiscriminant`	Discriminant analysis classification
`CompactClassificationDiscriminant`	Compact discriminant analysis class
`ClassificationPartitionedModel`	Cross-validated classification model

Topics

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

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.

Documentation