来自 SCIKIT 学习用户指南的 GridSearch 示例尝试给出错误

Question

Was trying to run the same code as per the SCIKIT user guide of Grid search but giving error.Quite surprised.

from sklearn.model_selection import GridSearchCV
from sklearn.calibration import CalibratedClassifierCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_moons
from sklearn.model_selection import cross_val_score
from sklearn.datasets import load_iris
X,y=make_moons()
calibrated_forest=CalibratedClassifierCV(base_estimator=RandomForestClassifier(n_estimators=10))
paramgrid={'base_estimator_max_depth':[2,4,6,8]}
search=GridSearchCV(calibrated_forest,paramgrid,cv=5)
search.fit(X,y)

Error message as below:

ValueError: Invalid parameter base_estimator_max_depth for estimator CalibratedClassifierCV(base_estimator=RandomForestClassifier(n_estimators=10)). Check the list of available parameters with `estimator.get_params().keys()`.

I tried with Iris data set which also gave the same error as above.

Then i used the make_moon dataset X,y and run the Random classifier as below.

clf = RandomForestClassifier(n_estimators=10, max_depth=2)
cross_val_score(clf, X, y, cv=5)

Got the output as below.

array([0.8 , 0.8 , 0.9 , 0.95, 0.95])

Looking strange and not sure what is happening and where iam wrong. Request help please.

Answer 1

Note the double score __ between base_estimator and a param:

from sklearn.model_selection import GridSearchCV
from sklearn.calibration import CalibratedClassifierCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_moons
from sklearn.model_selection import cross_val_score
from sklearn.datasets import load_iris
X,y=make_moons()
calibrated_forest=CalibratedClassifierCV(base_estimator=RandomForestClassifier(n_estimators=10))
paramgrid={'base_estimator__max_depth':[2,4,6,8]}
search=GridSearchCV(calibrated_forest,paramgrid,cv=5)
search.fit(X,y)
GridSearchCV(cv=5,
             estimator=CalibratedClassifierCV(base_estimator=RandomForestClassifier(n_estimators=10)),
             param_grid={'base_estimator__max_depth': [2, 4, 6, 8]})