plot_confusion_matrix

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method plot_confusion_matrix(models=None, dataset="test", normalize=False, title=None, figsize=None, filename=None, display=True) [source]

Plot a model's confusion matrix. For one model, the plot shows a heatmap. For multiple models, it compares TP, FP, FN and TN in a barplot (not implemented for multiclass classification tasks). Only for classification tasks.

Parameters:

models: str, sequence or None, optional (default=None) Name of the models to plot. If None, all models in the pipeline are selected. dataset: str, optional (default="test") Data set on which to calculate the confusion matrix. Options are "train" or "test". normalize: bool, optional (default=False) Whether to normalize the matrix. title: str or None, optional (default=None) Plot's title. If None, the title is left empty. figsize: tuple, optional (default=None) Figure's size, format as (x, y). If None, it adapts the size to plot's type. filename: str or None, optional (default=None) Name of the file. Use "auto" for automatic naming. If None, the figure is not saved. display: bool or None, optional (default=True) Whether to render the plot. If None, it returns the matplotlib figure.

Returns:

fig: matplotlib.figure.Figure Plot object. Only returned if display=None.

Example

from atom import ATOMClassifier

atom = ATOMClassifier(X, y)
atom.run(["Tree", "Bag"])
atom.Tree.plot_confusion_matrix(normalize=True)  # For one model

atom.plot_confusion_matrix()  # For multiple models