Make a confusion matrix from sample classifier predictions.
Make a confusion matrix and calculate accuracy of predicted vs. true values
for a set of samples classified using a sample classifier.
Parameters
- predictions : SampleData[ClassifierPredictions]
- Predicted values to plot on x axis. Should be predictions of
categorical data produced by a sample classifier.
- truth : MetadataColumn[Categorical]
- Metadata column (true values) to plot on y axis.
- missing_samples : Str % Choices('error', 'ignore'), optional
- How to handle missing samples in metadata. "error" will fail if missing
samples are detected. "ignore" will cause the feature table and
metadata to be filtered, so that only samples found in both files are
retained.
- palette : Str % Choices('YellowOrangeBrown', 'YellowOrangeRed', 'OrangeRed', 'PurpleRed', 'RedPurple', 'BluePurple', 'GreenBlue', 'PurpleBlue', 'YellowGreen', 'summer', 'copper', 'viridis', 'plasma', 'inferno', 'magma', 'sirocco', 'drifting', 'melancholy', 'enigma', 'eros', 'spectre', 'ambition', 'mysteriousstains', 'daydream', 'solano', 'navarro', 'dandelions', 'deepblue', 'verve', 'greyscale'), optional
- The color palette to use for plotting.
Returns
visualization : Visualization