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classmap (version 1.2.3)

Visualizing Classification Results

Description

Tools to visualize the results of a classification of cases. The graphical displays include stacked plots, silhouette plots, quasi residual plots, and class maps. Implements the techniques described and illustrated in Raymaekers, Rousseeuw and Hubert (2021), Class maps for visualizing classification results, Technometrics, appeared online. (open access) and Raymaekers and Rousseeuw (2021), Silhouettes and quasi residual plots for neural nets and tree-based classifiers, . Examples can be found in the vignettes: "Discriminant_analysis_examples","K_nearest_neighbors_examples", "Support_vector_machine_examples", "Rpart_examples", "Random_forest_examples", and "Neural_net_examples".

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Version

Install

install.packages('classmap')

Monthly Downloads

350

Version

1.2.3

License

GPL (>= 2)

Homepage

https://doi.org/10.1080/00401706.2021.1927849

Maintainer

Jakob Raymaekers

Last Published

April 23rd, 2023

Functions in classmap (1.2.3)

vcr.neural.train

Prepare for visualization of a neural network classification on training data.

vcr.forest.newdata

Prepare for visualization of a random forest classification on new data.

vcr.forest.train

Prepare for visualization of a random forest classification on training data

vcr.knn.newdata

Carry out a k-nearest neighbor classification on new data, and prepare to visualize its results.

Carry out discriminant analysis on training data, and prepare to visualize its results.

vcr.rpart.newdata

Prepare for visualization of an rpart classification on new data.

vcr.neural.newdata

Prepare for visualization of a neural network classification on new data.

Carry out discriminant analysis on new data, and prepare to visualize its results.

Make a vertically stacked mosaic plot of class predictions.

Carry out a k-nearest neighbor classification on training data, and prepare to visualize its results.

vcr.svm.newdata

Prepare for visualization of a support vector machine classification on new data.

Prepare for visualization of a support vector machine classification on training data.

vcr.rpart.train

Prepare for visualization of an rpart classification on training data.

Compute kernel matrix

Constructs feature vectors from a kernel matrix.

Draw a quasi residual plot of PAC versus a data feature

Build a confusion matrix from the output of a function vcr.*.*.

data_floralbuds

Floral buds data

data_bookReviews

Amazon book reviews data

Draw the silhouette plot of a classification

Draw the class map to visualize classification results.