In this example, we'll be plotting the ROC curve for our Naive Bayes classifier on the digits dataset. First, we're going
to fit our model with the data, then obtain the predicted probabilities y_probas
. Afterwhich, we can then pass the ground-truth values y
and y_probas
into scikitplot.metrics.plot_roc_curve
method.
In [1]:
from __future__ import absolute_import
import matplotlib.pyplot as plt
from sklearn.naive_bayes import GaussianNB
from sklearn.datasets import load_digits as load_data
# Import scikit-plot
import scikitplot as skplt
%pylab inline
pylab.rcParams['figure.figsize'] = (14, 14)
In [2]:
# Load the data
X, y = load_data(return_X_y=True)
# Create classifier instance then fit
nb = GaussianNB()
nb.fit(X,y)
# Get y_probabilities
y_probas = nb.predict_proba(X)
In [3]:
# Plot!
skplt.metrics.plot_roc_curve(y, y_probas, cmap='nipy_spectral')
plt.show()