On 2/14/20 5:47 PM, Paul Chike Ofoche via scikit-learn wrote:
Many thanks Nicolas and Andreas.
I was wondering whether this multioutput handling capability of the
RandomForestRegressor has been added recently. In order to verify, I
went on a fact-finding mission by re-running the exact same codes I
had in 2018 and noticed quite a number of changes. I guess that many
moons have passed since then!
For instance, sklearn.cross_validation has been deprecated since when
last I used it in 2018 (and replaced by sklearn.model_selection).
Also, such errors as:
i. ValueError: Expected 2D array, got scalar array instead:
array=6.5.
Reshape your data either using array.reshape(-1, 1) if your data has a
single feature or array.reshape(1, -1) if it contains a single sample.
and
ii. DataConversionWarning: A column-vector y was passed when a 1d
array was expected. Please change the shape of y to (n_samples,), for
example using ravel().
All of these were errors in 2018 already, you might not have had the
most up-to-date version then ;)
cross_validation was deprecated in 2016:
https://scikit-learn.org/dev/whats_new/v0.18.html#version-0-18
when passing a *scalar* and a *column-vector y* respectively are
entirely new from when last I made use of Python’s
RandomForestRegressor. Previously, they worked just fine without
throwing out any errors. I know that the “multioutputs” were handled
back in 2018 (I actually tested this capability back then), but I
assumed that the regressors were fit per target i.e. that there was no
correlation between targets.
I can't find a changelog entry but pretty sure this goes back to 2014 or
so. Definitely it was present in 2018.
Today, for comparison, I generated some random target outputs (three
columns) and using the same *random_state*, I ran the all-inclusive
multioutput prediction (with all three output targets simultaneously
vs. re-running each output prediction one at a time). The results are
different, implying that some form of correlation takes place amongst
the multioutput targets, when predicted together. (For completeness, I
display the first 28 predicted output values, from the multioutput
prediction as well as the single output predictions.
For my knowledge’s sake, could you please inform me about the
technique being employed now to take advantage of the correlations
between targets? Is it the Mahalanobis distance or some other metric?
In other words, could you please give me a hint as to the underlying
reason why the single output predictions differ from the multioutput
predictions? I am curious to know as this would finally fully quench
my appetite after nearly two years. I will have to retrace my steps
and get back to the good old Python ways (again). Thank you.
It doesn't explicitly use the correlation. The splitting criterion is is
the sum over the splitting criteria over the outputs. That means there's
an implicit regularization as the tree is shared between the targets.
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