plot_residuals_distribution with examples#
An example showing the plot_residuals_distribution function
used by a scikit-learn regressor.
9 # Authors: The scikit-plots developers
10 # SPDX-License-Identifier: BSD-3-Clause
Import scikit-plots#
16 from sklearn.datasets import (
17 load_diabetes as load_data,
18 )
19 from sklearn.linear_model import LinearRegression
20 from sklearn.model_selection import train_test_split
21
22 import numpy as np
23
24 np.random.seed(0) # reproducibility
25 # importing pylab or pyplot
26 import matplotlib.pyplot as plt
27
28 # Import scikit-plots
29 import scikitplot as sp
Loading the dataset#
35 # Load the data
36 X, y = load_data(return_X_y=True, as_frame=True)
37 X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.5, random_state=0)
Model Training#
43 # Create an instance of the LogisticRegression
44 model = LinearRegression().fit(X_train, y_train)
45
46 # Perform predictions
47 y_val_pred = model.predict(X_val)
Plot!#
53 # Plot!
54 ax = sp.metrics.plot_residuals_distribution(
55 y_val,
56 y_val_pred,
57 dist_type="normal",
58 save_fig=True,
59 save_fig_filename="",
60 # overwrite=True,
61 add_timestamp=True,
62 verbose=True,
63 )
Fitted mean-mu (μ): -4.4509
Fitted std (σ) : 55.2768
[INFO] Saving path to: /home/circleci/repo/galleries/examples/regression/result_images/plot_residuals_distribution_20250627_090925Z.png
[INFO] Plot saved to: /home/circleci/repo/galleries/examples/regression/result_images/plot_residuals_distribution_20250627_090925Z.png
References
The use of the following functions, methods, classes and modules is shown in this example:
Total running time of the script: (0 minutes 0.721 seconds)
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