plot_classifier_eval with examples#

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

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

Import scikit-plots#

 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 as mpl
 import matplotlib.pyplot as plt

 # Import scikit-plot
 import scikitplot as sp

Loading the dataset#

 # Load the data
 X, y = data_3_classes(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)

Model Training#

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

 # Perform predictions
 y_val_pred = model.predict(X_val)
 y_train_pred = model.predict(X_train)

Plot!#

 fig1 = sp.metrics.plot_classifier_eval(
     y_val,
     y_val_pred,
     labels=np.unique(y_train),
     figsize=(8, 2),
     title="Val",
     save_fig=True,
     save_fig_filename="",
     # overwrite=True,
     add_timestamp=True,
     # verbose=True,
 )
 # plt.show(block=True)
 fig2 = sp.metrics.plot_classifier_eval(
     y_train,
     y_train_pred,
     labels=np.unique(y_train),
     figsize=(8, 2),
     title="Train",
     save_fig=True,
     save_fig_filename="",
     # overwrite=True,
     add_timestamp=True,
     # verbose=True,
 )