Daniele Ongari

Fixed in https://github.com/scikit-learn-contrib/forest-confidence-interval/commit/ffa722702ed51f13b464fe8228cf72f456dd3db5 @arokem there are many redoundant issues mentioning the problem I solved: I guess you can close them pointing to this last commint

Hi, it looks like you have a MultiOutput regressor with 2 targets: my PR https://github.com/scikit-learn-contrib/forest-confidence-interval/pull/113 to address this problem was just merged Now you can obtain the errors with: ```...

@BSharmi can you please share the full dataset and training of your case? Did you try using a large number of `n_estimators` to avoid calibration? This could be an artifact...

I had the same issue with a `RandomForestRegressor`, but applying `StandardScaler` to the data SOLVED the issue: looks like this is a numerical issue.

Sure, but I would like to first reproduce and isolate the problem to see where the non-scaled inputs make the problem raise. Do you have an idea of which line...

In my testing if I use the following sample generator ``` def func(x, noise=0.1, factor=1): return (np.sqrt(x[0]) + x[0]*x[1] + 2*x[1] + sum(x) + noise*np.random.normal()) * factor ``` in a...

You raised a good point: in a noisy dataset (i.e., where a replicated sample could lead to different values for the target) a good model should have the IJK error...

@sphuber The error comes from inspecting the report (`verdi process report`). The workchain is submitted from a Jupyter notebook using the builder, and then submitting it. Of the ca. 200...

The "small epsi" solution by @mcstarioni worked for me. I just note that small-epsi values do overwrite themselves: my use case is that I have `a b c` where `b`...

Hi @tpflana, I guess you did install version 0.6 (i.e., with default `pip install forestci` and not the latest `master` version: there was not a release after I implemented my...