bat365 Homepage

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

expand all

Create Discriminant Analysis Model

`fitcdiscr`	Fit discriminant analysis classifier
`makecdiscr`	Construct discriminant analysis classifier from parameters
`compact`	Reduce size of discriminant analysis classifier

Improve Discriminant Analysis Model

cvshrink Cross-validate regularization of linear discriminant

Interpret Discriminant Analysis Model

`lime`	Local interpretable model-agnostic explanations (LIME) (Since R2020b)
`partialDependence`	Compute partial dependence (Since R2020b)
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`shapley`	Shapley values (Since R2021a)

Cross-Validate

`crossval`	Cross-validate discriminant analysis classifier
`kfoldEdge`	Classification edge for cross-validated classification model
`kfoldLoss`	Classification loss for cross-validated classification model
`kfoldfun`	Cross-validate function for classification
`kfoldMargin`	Classification margins for cross-validated classification model
`kfoldPredict`	Classify observations in cross-validated classification model

Measure Performance

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

Classify Observations

`predict`	Predict labels using discriminant analysis classifier
`resubPredict`	Predict resubstitution labels of discriminant analysis classification model
`classify`	Classify observations using 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
Learn about parametric classification methods.
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.