plot_roc_curve with examples#

An example showing the plot_roc_curve method used by a scikit-learn classifier

# Authors: scikit-plots developers
# License: MIT

from sklearn.datasets import (
    make_classification,
    load_breast_cancer as data_2_classes,
    load_iris as data_3_classes,
    load_digits as data_10_classes,
)
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_predict
import numpy as np; np.random.seed(0)
# importing pylab or pyplot
# import matplotlib as mpl
import matplotlib.pyplot as plt

# Import scikit-plot
import scikitplot as skplt

# 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)

# 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)

fig1 = skplt.metrics.plot_classifier_eval(
    y_val, y_val_pred,
    labels=np.unique(y_train),
    figsize=(8, 2),
    title='Val',
);
# plt.show(block=True)
fig2 = skplt.metrics.plot_classifier_eval(
    y_train, y_train_pred,
    labels=np.unique(y_train),
    figsize=(8, 2),
    title='Train',
);

# Save the combined figure as an image file
combined_fig = skplt.utils.combine_and_save_figures(
    (fig1, fig2),
    to_save=False
);

# Adjust layout to make sure everything fits
plt.tight_layout()
# Save the plot to a file
# plt.savefig('plot_classifier_eval_script.png')
# Display the plot
plt.show(block=True)