What will GridsearchCV choose if there are multiple estimators having the same score?How to graph grid scores from GridSearchCV?GridSeachCV with multiple scoring functions?Difference between GridSearchCV score and error functionSklearn - GridSearchCV with v_measure_score is NOT the sameHow to pass two estimator objects to sklearn's GridSearchCV so that they have the same parameters in each step?Using GridSearchCV and a Random Forest Regressor with the same parameters gives different resultsHow to set own scoring with GridSearchCV from sklearn for regression?Custom scoring function GridsearchCVGridsearchCV Negative Scorewhy GridSearchCv behave different in same code
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What will GridsearchCV choose if there are multiple estimators having the same score?
How to graph grid scores from GridSearchCV?GridSeachCV with multiple scoring functions?Difference between GridSearchCV score and error functionSklearn - GridSearchCV with v_measure_score is NOT the sameHow to pass two estimator objects to sklearn's GridSearchCV so that they have the same parameters in each step?Using GridSearchCV and a Random Forest Regressor with the same parameters gives different resultsHow to set own scoring with GridSearchCV from sklearn for regression?Custom scoring function GridsearchCVGridsearchCV Negative Scorewhy GridSearchCv behave different in same code
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;
I'm using RandomForestClassifier in sklearn, and using GridsearchCV for getting best estimator.
I'm wondering when there are many estimators (from simple one to complex one) having the same scores in GridsearchCV, what will be the resulted estimator out of GridsearchCV? The simplest one? or random one?
scikit-learn random-forest gridsearchcv
add a comment |
I'm using RandomForestClassifier in sklearn, and using GridsearchCV for getting best estimator.
I'm wondering when there are many estimators (from simple one to complex one) having the same scores in GridsearchCV, what will be the resulted estimator out of GridsearchCV? The simplest one? or random one?
scikit-learn random-forest gridsearchcv
add a comment |
I'm using RandomForestClassifier in sklearn, and using GridsearchCV for getting best estimator.
I'm wondering when there are many estimators (from simple one to complex one) having the same scores in GridsearchCV, what will be the resulted estimator out of GridsearchCV? The simplest one? or random one?
scikit-learn random-forest gridsearchcv
I'm using RandomForestClassifier in sklearn, and using GridsearchCV for getting best estimator.
I'm wondering when there are many estimators (from simple one to complex one) having the same scores in GridsearchCV, what will be the resulted estimator out of GridsearchCV? The simplest one? or random one?
scikit-learn random-forest gridsearchcv
scikit-learn random-forest gridsearchcv
asked Mar 27 at 12:02
GUANGYUAN PIAOGUANGYUAN PIAO
335 bronze badges
335 bronze badges
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1 Answer
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GridSearchCV
does not assess the model complexity (though that would be a neat feature). Neither does it choose among the best models randomly.
Instead, GridSearchCV
simply performs an np.argmin()
on the stored errors. See the corresponding line in the source code.
Now, according to the NumPy docs,
In case of multiple occurrences of the minimum values, the indices corresponding to the first occurrence are returned.
That is, GridSearchCV
will always select the first among the best models.
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
GridSearchCV
does not assess the model complexity (though that would be a neat feature). Neither does it choose among the best models randomly.
Instead, GridSearchCV
simply performs an np.argmin()
on the stored errors. See the corresponding line in the source code.
Now, according to the NumPy docs,
In case of multiple occurrences of the minimum values, the indices corresponding to the first occurrence are returned.
That is, GridSearchCV
will always select the first among the best models.
add a comment |
GridSearchCV
does not assess the model complexity (though that would be a neat feature). Neither does it choose among the best models randomly.
Instead, GridSearchCV
simply performs an np.argmin()
on the stored errors. See the corresponding line in the source code.
Now, according to the NumPy docs,
In case of multiple occurrences of the minimum values, the indices corresponding to the first occurrence are returned.
That is, GridSearchCV
will always select the first among the best models.
add a comment |
GridSearchCV
does not assess the model complexity (though that would be a neat feature). Neither does it choose among the best models randomly.
Instead, GridSearchCV
simply performs an np.argmin()
on the stored errors. See the corresponding line in the source code.
Now, according to the NumPy docs,
In case of multiple occurrences of the minimum values, the indices corresponding to the first occurrence are returned.
That is, GridSearchCV
will always select the first among the best models.
GridSearchCV
does not assess the model complexity (though that would be a neat feature). Neither does it choose among the best models randomly.
Instead, GridSearchCV
simply performs an np.argmin()
on the stored errors. See the corresponding line in the source code.
Now, according to the NumPy docs,
In case of multiple occurrences of the minimum values, the indices corresponding to the first occurrence are returned.
That is, GridSearchCV
will always select the first among the best models.
answered Mar 27 at 13:28
rvfrvf
6581 gold badge7 silver badges16 bronze badges
6581 gold badge7 silver badges16 bronze badges
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