plot_lift with examples#

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

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

 from sklearn.datasets import (
     load_iris as data_3_classes,
 )
 from sklearn.linear_model import LogisticRegression
 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 = 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)

 # Create an instance of the LogisticRegression
 model = LogisticRegression(max_iter=int(1e5), random_state=0).fit(X_train, y_train)

 # Perform predictions
 y_val_prob = model.predict_proba(X_val)

 # Plot!
 ax = sp.kds.plot_lift(
     y_val,
     y_val_prob,
     save_fig=True,
     save_fig_filename="",
     # overwrite=True,
     add_timestamp=True,
     # verbose=True,
 )