[Feature Suggestion] Add histogram plot of residual errors to ResidualsPlot

The current ResidualsPlot shows training and testing residuals as a scatter plot, by eye we can get an idea of whether more errors are above or below the 0 line. By adding a histogram of testing errors we might more clearly be able to tell if errors have a Normal distribution.

In the following examples I have some large positive and negative errors, from the histogram it looks as though I have a negatively skewed distribution which might tell me something about my training examples:

from yellowbrick.regressor import ResidualsPlot
fig, ax = plt.subplots(figsize=(8,6)); 
model = ResidualsPlot(clone_estimator(clf), ax=ax)
model.fit(X_train, y_train)
model.score(X_test, y_test)

# add histogram of residual errors
left, bottom, width, height = [0.65, 0.17, 0.2, 0.2]
ax2 = fig.add_axes([left, bottom, width, height])

testing_residuals = pd.Series(model.predict(X_test) - y_test)
testing_residuals.plot(kind="hist", bins=50, title="Residuals on Predicted", ax=ax2);
ax2.vlines(0, ymin=0, ymax=ax2.get_ylim()[1] ) # add x==0 line

model.poof()

It isn’t obvious where the best location would be for the histogram. Annoyingly I cannot get an alpha value for ax2 either (I’d hoped to make this semi-transparent so location was less of an issue).

[ianozsvald]

Well, thank you all too for putting this library together, I’ve got a bunch of my own hacky viz tools but you’ve built something far more useful here. @rebeccabilbro’s talk for us at the conference (and the book signing she joined me for) was ace 😃

[ianozsvald]

Overlaying test and train probably looks fine (given the PDF), that should be more comparable than having one stacked on the other? The PDF should look lovely regardless. I look forward to seeing it 😃