`QuantileDifferenceReason` and `StandardDeviationReason`

Hey! I was thinking if it would make sense to add two more reasons for regressions tasks, namely something like HighLeveragePointReason and HighStudentizedResidualReason.

Citing Wikipedia:

• Leverage is a measure of how far away the independent variable values of an observation are from those of the other observations. High-leverage points, if any, are outliers with respect to the independent variables (link)
• A studentized residual is the quotient resulting from the division of a residual by an estimate of its standard deviation. […] This is an important technique in the detection of outliers. (link)

[FBruzzesi]

The following results are mean scores across 500 different random states per reason-%shuffled pairs

reason	recall	precision	fpr	%shuffled
QuantileDifferenceReason	0.31	0.05	0.051	1%
QuantileDifferenceReason	0.40	0.24	0.042	5%
QuantileDifferenceReason	0.37	0.42	0.033	10%
QuantileDifferenceReason	0.29	0.62	0.023	20%
BoxplotReason	0.10	0.07	0.004	1%
BoxplotReason	0.13	0.34	0.003	5%
BoxplotReason	0.11	0.47	0.002	10%
BoxplotReason	0.06	0.52	0.0007	20%
StandardDeviationReason	0.29	0.072	0.037	1%
StandardDeviationReason	0.34	0.296	0.029	5%
StandardDeviationReason	0.30	0.461	0.023	10%
StandardDeviationReason	0.26	0.703	0.014	20%

[FBruzzesi]

@koaning finally found the time to write a notebook, you can find it here.