This function will produce a bar plot to show the true positives (TP) versus the false negative (FN) for each target class. Both correct and incorrect classifications are color-coded to show the predicted class.
Inputs:
C - an integer matrix of the confusion data to plot, with ground truth labels in rows and predicted labels in columns
c_labels - a cell array of class labels, given in the same order as in C
cmap - [optional] a colormap matrix specifying colors to use in displaying each class from c_labels
max_y - [optional] integer maximum value for y axis in the produced plot
Outputs:
b_fig - a handle to the figure produced
引用格式
D. Heise and H. Bear, "Evaluating the Potential and Realized Impact of Data Augmentations", submitted to 2023 IEEE Symposium Series on Computational Intelligence, in review.
MATLAB 版本兼容性
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R2023a
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