plot_learning_curve with examples#

An example showing the plot_learning_curve function used by a scikit-learn classifier.

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

Import scikit-plots#

16 from sklearn.datasets import (
17     load_digits as data_10_classes,
18 )
19 from sklearn.linear_model import LogisticRegression
20
21 import numpy as np
22
23 np.random.seed(0)  # reproducibility
24 # importing pylab or pyplot
25 import matplotlib.pyplot as plt
26
27 # Import scikit-plot
28 import scikitplot as sp

Loading the dataset#

34 # Load the data
35 X, y = data_10_classes(return_X_y=True, as_frame=False)

Model Training#

41 # Create an instance of the LogisticRegression
42 model = LogisticRegression(max_iter=int(1e5), random_state=0)

Plot!#

48 # Plot!
49 ax = sp.estimators.plot_learning_curve(
50     model,
51     X,
52     y,
53     save_fig=True,
54     save_fig_filename="",
55     # overwrite=True,
56     add_timestamp=True,
57     # verbose=True,
58 )
LogisticRegression Learning Curves

Tags: model-type: classification model-workflow: model evaluation plot-type: line plot-type: learning curve level: beginner purpose: showcase

Total running time of the script: (0 minutes 1.393 seconds)

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