Classification and Discriminant Analysis: Difference between revisions
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:[[analysis]] - Graphical user interface for data analysis. | :[[analysis]] - Graphical user interface for data analysis. | ||
:[[knn]] - K-nearest neighbor classifier. | :[[knn]] - K-nearest neighbor classifier. | ||
:[[ | :[[lda]] - Linear Discriminant Analysis. | ||
:[[plsda]] - Partial least squares discriminant analysis. | :[[plsda]] - Partial least squares discriminant analysis. | ||
:[[lregda]] - Predictions based on Logistic Regression (LREGDA) classification models. | :[[lregda]] - Predictions based on Logistic Regression (LREGDA) classification models. | ||
Latest revision as of 10:17, 8 December 2023
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.
- lda - Linear Discriminant Analysis.
- plsda - Partial least squares discriminant analysis.
- lregda - Predictions based on Logistic Regression (LREGDA) classification models.
- simca - Soft Independent Method of Class Analogy.
- svmda - SVM Support Vector Machine for classification.
Model Analysis and Calculation Utilities
- confusionmatrix - Create a confusion matrix.
- confusiontable - Create a confusion table.
- 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)