Comments (4)
MillHaus33
commented on June 13, 2024
I have faced a similar issue. I have a target variable with a long right tail. If I cap my target variable the forest error does not have missing values. As I raise my target variable cap the missing variables increase. Any insight as to why this issue occurs would be useful! Thanks!
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hermanc1
commented on June 13, 2024
We are having the same problem. Has anyone found a solution to this?
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lcol90
commented on June 13, 2024
Is there any solution for this problem? My NaNs seem to derive from eb_prior = gfit(variances, sigma) in calibrateEB. Thanks in advance!
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DavidLloydNGP
commented on June 13, 2024
Similar to @MillHaus33 I see this problem with a target variable with a long-tailed distribution (or more specifically, quite a few large value outliers). The problem appears for certain test/train splits, so it's the case that the distribution of the target variable always causes the problem. From a practical point of view, there may some kind of data transformation that doesn't impair the model performance but also avoids this problem. I have not found evidence to support this yet though.
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Related Issues (20)
- cannot import name '_get_n_samples_bootstrap' HOT 3
- ValueError on multiple output problems HOT 1
- Sum taken over wrong axis HOT 2
- Can this package be adapted to perform Thompson sampling?
- Compatibility issues with scikit-learn 0.24.2 HOT 1
- Benchmarking confidence intervals HOT 1
- Array dimensions incorrect for confidence intervals HOT 12
- New Release HOT 1
- progress indicator?
- random_forest_error() does not work without scalers.
- Unnecessary usage of training data?
- Warning: sklearn.ensemble.forest module is deprecated in version 0.22 HOT 3
- Can't uninstall forestci HOT 1
- Overflow errors HOT 4
- Not compatible with SKLearn version 0.22.1 HOT 4
- amount of trees needed to work
- All 0's in `g_eta_raw`.
- forest error are all NaN HOT 8
- Applicability to non-binary classification tasks
- Allow general bagging estimators
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