plot_residuals_distribution with examples#
An example showing the plot_residuals_distribution function
used by a scikit-learn regressor.
# Authors: The scikit-plots developers
# SPDX-License-Identifier: BSD-3-Clause
from sklearn.datasets import (
make_classification,
load_diabetes as load_data,
)
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.svm import LinearSVR
from sklearn.ensemble import RandomForestRegressor
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
import scikitplot.probscale as probscale
# Load the data
X, y = load_data(return_X_y=True, as_frame=True)
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.5, random_state=0)
# Create an instance of the LogisticRegression
model = LinearRegression().fit(X_train, y_train)
# Perform predictions
y_val_pred = model.predict(X_val)
# Plot!
ax = sp.metrics.plot_residuals_distribution(
y_val, y_val_pred, dist_type='normal'
);
# 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)
Fitted mean-mu (μ): -4.4509
Fitted std (σ) : 55.2768
References
The use of the following functions, methods, classes and modules is shown in this example:
Total running time of the script: (0 minutes 0.899 seconds)
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