Classification and Discriminant Analysis: Difference between revisions
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==Top-Level Classification / Discriminant Analysis Tools== | ==Top-Level Classification / Discriminant Analysis Tools== | ||
:[[analysis]] - Graphical user interface for data analysis. | :[[analysis]] - Graphical user interface for data analysis. | ||
:[[knn]] - K-nearest neighbor classifier. | |||
:[[plsda]] - Partial least squares discriminant analysis. | |||
:[[simca]] - Soft Independent Method of Class Analogy. | :[[simca]] - Soft Independent Method of Class Analogy. | ||
:[[ | :[[svmda]] - SVM Support Vector Machine for classification. | ||
==Model Analysis and Calculation Utilities== | ==Model Analysis and Calculation Utilities== | ||
:[[class2logical]] - Create a PLSDA logical block from class assignments. | :[[class2logical]] - Create a PLSDA logical block from class assignments. | ||
Revision as of 11:27, 1 September 2010
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.
- knn - K-nearest neighbor classifier.
- plsda - Partial least squares discriminant analysis.
- simca - Soft Independent Method of Class Analogy.
- svmda - SVM Support Vector Machine for classification.
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)