Comments (4)
kpolimis
commented on June 7, 2024
license changed to : MIT open source license
from forest-confidence-interval.
arokem
commented on June 7, 2024
Addressed in 61abf19
from forest-confidence-interval.
DannyArends
commented on June 7, 2024
The the license provided is still NOT the official MIT license, as provided by the open source foundation at https://opensource.org/licenses/MIT
The words: "All rights reserved." do NOT occur in the MIT license, and should be removed.
The first line should read, (remove the (c), the comma (,) and please add the affiliation to the first line):
Copyright 2016 Ariel Rokem, Bryna Hazelton, Kivan Polimis (The University of Washington, eScience Institute)
When you want to use the MIT license, please copy the license verbatim from: https://opensource.org/licenses/MIT
from forest-confidence-interval.
arokem
commented on June 7, 2024
Sorry about that. How's that? #53
from forest-confidence-interval.
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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from forest-confidence-interval.