Using GridSearchCV with AdaBoost and DecisionTreeClassifier

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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.

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