Visualizing Classification Results
Draw the class map to visualize classification results.
Build a confusion matrix from the output of a function vcr.*.*.
Constructs feature vectors from a kernel matrix.
Compute kernel matrix
Draws a predictions correlation plot, which visualizes the correlation...
Make a predictions plot
Draw a quasi residual plot of PAC versus a data feature
Draw the silhouette plot of a classification
Make a vertically stacked mosaic plot of class predictions.
Carry out discriminant analysis on new data, and prepare to visualize ...
Carry out discriminant analysis on training data, and prepare to visua...
Prepare for visualization of a random forest classification on new dat...
Prepare for visualization of a random forest classification on trainin...
Carry out a k-nearest neighbor classification on new data, and prepare...
Carry out a k-nearest neighbor classification on training data, and pr...
Prepare for visualization of a neural network classification on new da...
Prepare for visualization of a neural network classification on traini...
Prepare for visualization of an rpart classification on new data.
Prepare for visualization of an rpart classification on training data.
Prepare for visualization of a support vector machine classification o...
Prepare for visualization of a support vector machine classification o...
Tools to visualize the results of a classification or a regression. The graphical displays include stacked plots, silhouette plots, quasi residual plots, class maps, predictions plots, and predictions correlation plots. Implements the techniques described and illustrated in Raymaekers J., Rousseeuw P.J., Hubert M. (2022). Class maps for visualizing classification results. \emph{Technometrics}, 64(2), 151–165. \doi{10.1080/00401706.2021.1927849} (open access), Raymaekers J., Rousseeuw P.J.(2022). Silhouettes and quasi residual plots for neural nets and tree-based classifiers. \emph{Journal of Computational and Graphical Statistics}, 31(4), 1332–1343. \doi{10.1080/10618600.2022.2050249}, and Rousseeuw, P.J. (2025). Explainable Linear and Generalized Linear Models by the Predictions Plot. <doi:10.48550/arXiv.2412.16980> (open access). Examples can be found in the vignettes: "Discriminant_analysis_examples","K_nearest_neighbors_examples", "Support_vector_machine_examples", "Rpart_examples", "Random_forest_examples", "Neural_net_examples", and "predsplot_examples".
Useful links