Classification and Discriminant Analysis: Difference between revisions
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These methods help separate samples into classes and develop models which can be used to predict which class a new sample belongs to. | These methods help separate samples into classes and develop models which can be used to predict which class a new sample belongs to. | ||
==Top-Level Classification / Discriminant Analysis Tools== | |||
:[[analysis]] - Graphical user interface for data analysis. | :[[analysis]] - Graphical user interface for data analysis. | ||
:[[simca]] - Soft Independent Method of Class Analogy. | :[[simca]] - Soft Independent Method of Class Analogy. | ||
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:[[knn]] - K-nearest neighbor classifier. | :[[knn]] - K-nearest neighbor classifier. | ||
==Model Analysis and Calculation Utilities== | |||
:[[class2logical]] - Create a PLSDA logical block from class assignments. | :[[class2logical]] - Create a PLSDA logical block from class assignments. | ||
:[[discrimprob]] - Discriminate probabilities for continuous predicted values. | :[[discrimprob]] - Discriminate probabilities for continuous predicted values. | ||
Revision as of 14:41, 4 September 2008
These methods help separate samples into classes and develop models which can be used to predict which class a new sample belongs to.
Top-Level Classification / Discriminant Analysis Tools
- analysis - Graphical user interface for data analysis.
- simca - Soft Independent Method of Class Analogy.
- plsda - Partial least squares discriminant analysis.
- knn - K-nearest neighbor classifier.
Model Analysis and Calculation Utilities
- class2logical - Create a PLSDA logical block from class assignments.
- discrimprob - Discriminate probabilities for continuous predicted values.
- plsdaroc - Calculate and display ROC curves for PLSDA model.
- plsdthres - Bayesian threshold determination for PLS Discriminate Analysis.
(Sub topic of PLS_Toolbox_Topics)