plot_elbow#

scikitplot.api.estimators.plot_elbow(clf, X, *, cluster_ranges=None, show_cluster_time=True, n_jobs=1, title='Elbow Curves', title_fontsize='large', text_fontsize='medium', **kwargs)[source]#

Plot the elbow curve for different values of K in KMeans clustering.

Parameters:

clfobject: A clusterer instance with fit, fit_predict, and score methods, and an n_clusters hyperparameter. Typically an instance of sklearn.cluster.KMeans.
Xarray-like of shape (n_samples, n_features): The data to cluster, where n_samples is the number of samples and n_features is the number of features.
cluster_rangeslist of int or None, optional, default=range(1, 12, 2): List of values for n_clusters over which to plot the explained variances.
show_cluster_timebool, optional: Whether to include a plot of the time taken to cluster for each value of K.
n_jobsint, optional, default=1: The number of jobs to run in parallel.
titlestr, optional, default=”Elbow Plot”: The title of the generated plot.
title_fontsizestr or int, optional, default=”large”: Font size of the title. Accepts Matplotlib font sizes, such as “small”, “medium”, “large”, or an integer value.
text_fontsizestr or int, optional, default=”medium”: Font size of the text labels. Accepts Matplotlib font sizes, such as “small”, “medium”, “large”, or an integer value.
**kwargs: dict: Generic keyword arguments.

Returns:

axmatplotlib.axes.Axes: The axes on which the plot was drawn.

Other Parameters:

axmatplotlib.axes.Axes, optional, default=None

The axis to plot the figure on. If None is passed in the current axes will be used (or generated if required).

Added in version 0.4.0.

figmatplotlib.pyplot.figure, optional, default: None

The figure to plot the Visualizer on. If None is passed in the current plot will be used (or generated if required).

Added in version 0.4.0.

figsizetuple, optional, default=None

Width, height in inches. Tuple denoting figure size of the plot e.g. (12, 5)

Added in version 0.4.0.

nrowsint, optional, default=1

Number of rows in the subplot grid.

Added in version 0.4.0.

ncolsint, optional, default=1

Number of columns in the subplot grid.

Added in version 0.4.0.

plot_stylestr, optional, default=None

Check available styles with “plt.style.available”. Examples include: [‘ggplot’, ‘seaborn’, ‘bmh’, ‘classic’, ‘dark_background’, ‘fivethirtyeight’, ‘grayscale’, ‘seaborn-bright’, ‘seaborn-colorblind’, ‘seaborn-dark’, ‘seaborn-dark-palette’, ‘tableau-colorblind10’, ‘fast’].

Added in version 0.4.0.

show_figbool, default=True

Show the plot.

Added in version 0.4.0.

save_figbool, default=False

Save the plot.

Added in version 0.4.0.

save_fig_filenamestr, optional, default=’’

Specify the path and filetype to save the plot. If nothing specified, the plot will be saved as png inside result_images under to the current working directory. Defaults to plot image named to used func.__name__.

Added in version 0.4.0.

overwritebool, optional, default=True

If False and a file exists, auto-increments the filename to avoid overwriting.

Added in version 0.4.0.

add_timestampbool, optional, default=False

Whether to append a timestamp to the filename. Default is False.

Added in version 0.4.0.

verbosebool, optional

If True, enables verbose output with informative messages during execution. Useful for debugging or understanding internal operations such as backend selection, font loading, and file saving status. If False, runs silently unless errors occur.

Default is False.

Added in version 0.4.0: The verbose parameter was added to control logging and user feedback verbosity.

Examples

>>> from sklearn.cluster import KMeans
>>> from sklearn.datasets import load_iris as data_3_classes
>>> import scikitplot as skplt
>>> X, y = data_3_classes(return_X_y=True, as_frame=False)
>>> kmeans = KMeans(random_state=0)
>>> skplt.estimators.plot_elbow(
>>>     kmeans,
>>>     X,
>>>     cluster_ranges=range(1, 10),
>>> );

(Source code, png)

plot_elbow#

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