evalplot#

scikitplot.snsx.evalplot(data=None, *, x=None, y=None, hue=None, kind=None, weights=None, labels=None, threshold=0.5, allow_probs=False, hue_order=None, hue_norm=None, palette=None, color=None, fill=False, baseline=False, line_kws=None, log_scale=None, legend=False, ax=None, cbar_kws=None, cbar=True, cbar_ax=None, text_kws=None, image_kws=None, annot_kws=None, annot=True, fmt='', digits=4, common_norm=None, verbose=False, **kwargs)[source]#

Visualization of the Confusion Matrix [1] alongside a text report showing key classification metrics.

For guidance on interpreting these plots, refer to the Model Evaluation Guide.

Parameters:
datapandas.DataFrame, numpy.ndarray, mapping, or sequence

Input data structure. Either a long-form collection of vectors that can be assigned to named variables or a wide-form dataset that will be internally reshaped.

x, yvectors or keys in data

Variables that specify positions on the x and y axes.

huevector or key in data

Semantic variable that is mapped to determine the color of plot elements.

kind{‘all’, ‘classification_report’, ‘confusion_matrix’} or None, default=None

Which visualization to draw.

weightsvector or key in data

Sample weights passed to the underlying scikit-learn metric functions.

labelsarray-like, optional

Class label ordering. When provided, it is forwarded to sklearn.metrics.confusion_matrix and sklearn.metrics.classification_report.

thresholdfloat, default=0.5

Threshold used to convert probabilities into predicted class labels when allow_probs=True.

allow_probsbool, default=False

If True, interpret y as probabilities in [0, 1] and derive predicted labels via y > threshold. This requires binary classification. Behavior like ‘y > thr’ see numpy.argmax.

hue_ordervector of strings

Specify the order of processing and plotting for categorical levels of the hue semantic.

hue_normtuple or matplotlib.colors.Normalize

Either a pair of values that set the normalization range in data units or an object that will map from data units into a [0, 1] interval. Usage implies numeric mapping.

palettestring, list, dict, or matplotlib.colors.Colormap

Method for choosing the colors to use when mapping the hue semantic. String values are passed to color_palette. List or dict values imply categorical mapping, while a colormap object implies numeric mapping.

colormatplotlib color

Single color specification for when hue mapping is not used. Otherwise, the plot will try to hook into the matplotlib property cycle.

fillbool or None

If True, fill in the area under univariate density curves or between bivariate contours. If None, the default depends on multiple.

{line}_kwsdictionaries

Additional keyword arguments to pass to plt.plot.

log_scalebool or number, or pair of bools or numbers

Set axis scale(s) to log. A single value sets the data axis for any numeric axes in the plot. A pair of values sets each axis independently. Numeric values are interpreted as the desired base (default 10). When None or False, seaborn defers to the existing Axes scale.

legendbool

If False, suppress the legend for semantic variables.

axmatplotlib.axes.Axes

Pre-existing axes for the plot. Otherwise, call matplotlib.pyplot.gca internally.

cbar_kwsdict

Additional parameters passed to matplotlib.figure.Figure.colorbar.

cbarbool

If True, add a colorbar to annotate the color mapping in a bivariate plot. Note: Does not currently support plots with a hue variable well.

cbar_axmatplotlib.axes.Axes

Pre-existing axes for the colorbar.

text_kwsdict, optional

Keyword arguments passed to matplotlib.axes.Axes.text when rendering the classification report (and for confusion-matrix annotations).

image_kwsdict, optional

Keyword arguments passed to matplotlib.axes.Axes.imshow when drawing the confusion matrix. Recognized keys:

cmapNone, str or matplotlib.colors.Colormap, optional, default=None

Colormap used for plotting. Options include ‘viridis’, ‘PiYG’, ‘plasma’, ‘inferno’, ‘nipy_spectral’, etc. See Matplotlib Colormap documentation for available choices.

annot_kwsdict of key, value mappings, optional

Keyword arguments for matplotlib.axes.Axes.text when annot is True.

annotbool or rectangular dataset, optional

If True, write the data value in each cell. If an array-like with the same shape as data, then use this to annotate the heatmap instead of the data. Note that DataFrames will match on position, not index.

fmtstr, optional, default=’’

Formatting spec for confusion matrix annotations (e.g., '.2f'). When normalize is not None and fmt is empty, it defaults to '.2f'.

digitsint, optional, default=4

Number of digits for formatting output floating point values. When output_dict is True, this will be ignored and the returned values will not be rounded.

normalize{‘true’, ‘pred’, ‘all’, None}, optional, default=None

Normalization mode passed to sklearn.metrics.confusion_matrix when kind includes 'confusion_matrix'.

  • ‘true’: Normalizes by true (actual) values.

  • ‘pred’: Normalizes by predicted values.

  • ‘all’: Normalizes by total values.

  • None: No normalization.

common_normbool

If True, scale each conditional density by the number of observations such that the total area under all densities sums to 1. Otherwise, normalize each density independently.

zero_division{“warn”, 0.0, 1.0, np.nan}, default=”warn”

Sets the value to return when there is a zero division. If set to “warn”, this acts as 0, but warnings are also raised.

output_dictbool, default=False

If True, return output as dict.

verbosebool, optional, default=False

Whether to be verbose.

kwargs

Other keyword arguments are passed to one of the following matplotlib functions:

Returns:
matplotlib.axes.Axes

The matplotlib axes containing the plot.

Warning

Some function parameters are experimental prototypes. These may be modified, renamed, or removed in future library versions. Use with caution and check documentation for the latest updates.

Parameters:
Return type:

Axes

See also

sklearn.metrics.confusion_matrix
sklearn.metrics.classification_report

Notes

  • When kind='all', a 1x2 dashboard is created. If the provided ax is not the only axes in its figure, a new figure is created.

  • If multiple semantic subsets are present (e.g., multiple hue levels), confusion-matrix-style plots do not overlay cleanly; only the first subset is plotted and a warning is emitted.

References

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