Using GridSearchCV with AdaBoost and DecisionTreeClassifier

问题

I am attempting to tune an AdaBoost Classifier ("ABT") using a DecisionTreeClassifier ("DTC") as the base_estimator. I would like to tune both ABT and DTC parameters simultaneously, but am not sure how to accomplish this - pipeline shouldn't work, as I am not "piping" the output of DTC to ABT. The idea would be to iterate hyper parameters for ABT and DTC in the GridSearchCV estimator.

How can I specify the tuning parameters correctly?

I tried the following, which generated an error below.

[IN]
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.grid_search import GridSearchCV

param_grid = {dtc__criterion : ["gini", "entropy"],
              dtc__splitter :   ["best", "random"],
              abc__n_estimators: [none, 1, 2]
             }


DTC = DecisionTreeClassifier(random_state = 11, max_features = "auto", class_weight = "auto",max_depth = None)

ABC = AdaBoostClassifier(base_estimator = DTC)

# run grid search
grid_search_ABC = GridSearchCV(ABC, param_grid=param_grid, scoring = 'roc_auc')

[OUT]
ValueError: Invalid parameter dtc for estimator AdaBoostClassifier(algorithm='SAMME.R',
      base_estimator=DecisionTreeClassifier(class_weight='auto', criterion='gini', max_depth=None,
        max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
        min_samples_split=2, min_weight_fraction_leaf=0.0,
        random_state=11, splitter='best'),
      learning_rate=1.0, n_estimators=50, random_state=11)

回答1:

There are several things wrong in the code you posted:

1. The keys of the param_grid dictionary need to be strings. You should be getting a NameError.
2. The key "abc__n_estimators" should just be "n_estimators": you are probably mixing this with the pipeline syntax. Here nothing tells Python that the string "abc" represents your AdaBoostClassifier.
3. None (and not none) is not a valid value for n_estimators. The default value (probably what you meant) is 50.

Here's the code with these fixes. To set the parameters of your Tree estimator you can use the "__" syntax that allows accessing nested parameters.

from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.grid_search import GridSearchCV

param_grid = {"base_estimator__criterion" : ["gini", "entropy"],
              "base_estimator__splitter" :   ["best", "random"],
              "n_estimators": [1, 2]
             }


DTC = DecisionTreeClassifier(random_state = 11, max_features = "auto", class_weight = "auto",max_depth = None)

ABC = AdaBoostClassifier(base_estimator = DTC)

# run grid search
grid_search_ABC = GridSearchCV(ABC, param_grid=param_grid, scoring = 'roc_auc')

Also, 1 or 2 estimators does not really make sense for AdaBoost. But I'm guessing this is not the actual code you're running.

Hope this helps.

来源：https://stackoverflow.com/questions/32210569/using-gridsearchcv-with-adaboost-and-decisiontreeclassifier