plot_residuals_distribution with examples#

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

from sklearn.datasets import (
    load_diabetes as load_data,
)
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

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 = 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",
    save_fig=True,
    save_fig_filename="",
    # overwrite=True,
    add_timestamp=True,
    verbose=True,
)