plot_roc_curve

scikitplot.metrics.plot_roc_curve(y_true, y_probas, title='ROC Curves', curves=('micro', 'macro', 'each_class'), ax=None, figsize=None, cmap='nipy_spectral', title_fontsize='large', text_fontsize='medium')

Generates the ROC curves from labels and predicted scores/probabilities

Parameters:

• y_true (array-like, shape (n_samples)) – Ground truth (correct) target values.

• y_probas (array-like, shape (n_samples, n_classes)) – Prediction probabilities for each class returned by a classifier.

• title (string, optional) – Title of the generated plot. Defaults to “ROC Curves”.

• curves (array-like) – A listing of which curves should be plotted on the resulting plot. Defaults to ("micro", "macro", "each_class") i.e. “micro” for micro-averaged curve, “macro” for macro-averaged curve

• ax (matplotlib.axes.Axes, optional) – The axes upon which to plot the curve. If None, the plot is drawn on a new set of axes.

• figsize (2-tuple, optional) – Tuple denoting figure size of the plot e.g. (6, 6). Defaults to None.

• cmap (string or matplotlib.colors.Colormap instance, optional) – Colormap used for plotting the projection. View Matplotlib Colormap documentation for available options. https://matplotlib.org/users/colormaps.html

• title_fontsize (string or int, optional) – Matplotlib-style fontsizes. Use e.g. “small”, “medium”, “large” or integer-values. Defaults to “large”.

• text_fontsize (string or int, optional) – Matplotlib-style fontsizes. Use e.g. “small”, “medium”, “large” or integer-values. Defaults to “medium”.

Returns:

The axes on which the plot was

drawn.

Return type:

ax (matplotlib.axes.Axes)

Example

>>> import scikitplot as skplt
>>> nb = GaussianNB()
>>> nb = nb.fit(X_train, y_train)
>>> y_probas = nb.predict_proba(X_test)
>>> skplt.metrics.plot_roc_curve(y_test, y_probas)