[MRG+1] Convert y in GradientBoosting to float64 instead of float32 #13524
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jnothman
approved these changes
Mar 27, 2019
| @@ -1432,7 +1432,7 @@ def fit(self, X, y, sample_weight=None, monitor=None): | |||
| self._clear_state() | |||
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| # Check input | |||
| X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'], dtype=DTYPE) | |||
| X = check_array(X, accept_sparse=['csr', 'csc', 'coo'], dtype=DTYPE) | |||
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It's worth commenting why check_X_y is not used.
thomasjpfan
approved these changes
Mar 28, 2019
| @@ -44,7 +44,7 @@ | |||
| from time import time | |||
| from ..model_selection import train_test_split | |||
| from ..tree.tree import DecisionTreeRegressor | |||
| from ..tree._tree import DTYPE | |||
| from ..tree._tree import DTYPE, DOUBLE | |||
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It is interesting how this is our naming convention for the dtypes of X and y. In the future we may consider X_DTYPE and Y_DTYPE.
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Yeah I know. You're the second +1 here now @thomasjpfan, wanna push the green button? ;)
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Ta @adrinjalali |
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…32 (scikit-learn#13524)" This reverts commit 2502111.
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…32 (scikit-learn#13524)" This reverts commit 2502111.
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Fixes #9098
If not float64, the decision trees would each convert it to a double.