plot_elbow with examples#

An example showing the plot_elbow function used by a scikit-learn clusterer.

# Authors: The scikit-plots developers
# SPDX-License-Identifier: BSD-3-Clause

Load the dataset#

We will start by loading the iris dataset.

from sklearn.datasets import (
    make_classification,
    load_breast_cancer as data_2_classes,
    load_iris as data_3_classes,
    load_digits as data_10_classes,
)
from sklearn.model_selection import train_test_split
from sklearn.cluster import KMeans
from sklearn.model_selection import cross_val_predict

import numpy as np; np.random.seed(0)  # reproducibility
# importing pylab or pyplot
import matplotlib.pyplot as plt

# Import scikit-plot
import scikitplot as sp

# Load the data
X, y = data_3_classes(return_X_y=True, as_frame=False)
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.5, random_state=0)

Model Training#

Create an instance of the LogisticRegression

model = KMeans(n_clusters=4, random_state=1)

Visualize the results#

Plot!

ax = sp.estimators.plot_elbow(
    model,
    X_train,
    cluster_ranges=range(1, 11)
);

# Adjust layout to make sure everything fits
plt.tight_layout()

# Save the plot with a filename based on the current script's name
# sp.api._utils.save_plot()

# Display the plot
plt.show(block=True)