Using GridSearchCV with AdaBoost and DecisionTreeClassifier

Question

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) 

Answer 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.

Answer 2

Trying to provide a shorter (and hopefully generic) answer.

---

If you want to grid search within a BaseEstimator for the AdaBoostClassifier e.g. varying the max_depth or min_sample_leaf of a DecisionTreeClassifier estimator, then you have to use a special syntax in the parameter grid.

abc = AdaBoostClassifier(base_estimator=DecisionTreeClassifier())  parameters = {'base_estimator__max_depth':[i for i in range(2,11,2)],               'base_estimator__min_samples_leaf':[5,10],               'n_estimators':[10,50,250,1000],               'learning_rate':[0.01,0.1]}  clf = GridSearchCV(abc, parameters,verbose=3,scoring='f1',n_jobs=-1) clf.fit(X_train,y_train) 

So, note the 'base_estimator__max_depth' and 'base_estimator__min_samples_leaf' keys in the parameters dictionary. That's the way to access the hyperparameters of a BaseEstimator for an ensemble algorithm like AdaBoostClassifier when you are doing a grid search. Note the __ double underscore notation in particular. Other two keys in the parameters are the regular AdaBoostClassifier parameters.