modelplot#

scikitplot.seaborn.modelplot(data=None, *, x=None, y=None, hue=None, x_estimator=None, kind=None, hue_order=None, hue_norm=None, palette=None, color=None, fill=False, baseline=False, line_kws=None, log_scale=None, legend=True, ax=None, cbar=True, cbar_ax=None, cbar_kws=None, text_kws=None, image_kws=None, digits=None, common_norm=None, verbose=False, **kwargs)[source]#

Model Instance and/or Attributes Visualization.

Supported attributes:

  • feature_names_in_

  • feature_importances_

  • coef_

  • explained_variance_ratio_

  • eigenvalues_

  • eigenvectors_

Parameters:
x_estimatormodel

An instance so access the fitted model attribute(s).

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{‘feature_importances’} or None, default=None

Kind of plot to make.

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.

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.

cbar_kwsdict

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

text_kwsdict

Parameters that control the 'classification_report' visualization, passed to text.

image_kwsdict

Parameters that control the 'confusion_matrix' visualization, passed to imshow. 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.

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.

output_dictbool, default=False

If True, return output as dict.

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.

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.

verbosebool, optional, default=False

Whether to be verbose.

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

Normalizes the confusion matrix according to the specified mode. Defaults to None.

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

  • ‘pred’: Normalizes by predicted values.

  • ‘all’: Normalizes by total values.

  • None: No normalization.

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

FastICA

A fast algorithm for Independent Component Analysis.

IncrementalPCA

Incremental Principal Component Analysis.

NMF

Non-Negative Matrix Factorization.

PCA

Principal component analysis (PCA).

KernelPCA

Kernel Principal component analysis (KPCA).

SparsePCA

Sparse Principal Components Analysis (SparsePCA).

TruncatedSVD

Dimensionality reduction using truncated SVD.

permutation_importance

Permutation importance for feature evaluation [BRE].

partial_dependence

Compute Partial Dependence values.

PartialDependenceDisplay

Partial dependence visualization.

PartialDependenceDisplay.from_estimator

Plot Partial Dependence.

DecisionBoundaryDisplay

Decision boundary visualization.

DecisionBoundaryDisplay.from_estimator

Plot decision boundary given an estimator.

References