title: sklearn Boosting: cross-validation to find optimal number of estimators without restarting everytime
body: <p>In Python sklearn ensemble library, I want to train my data using some boosting method (say Adaboost). As I would like to know the optimal number of estimators, I plan to do a cv with different number of estimators each time. However, it seems doing it the following way is redundant:</p>
  
  <pre><code> for n in [50,100,150,200,250,300]:
       model = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1),n_estimators=n) 
       cross_val_score(model,x,y,k=5)
  </code></pre>
  
  <p>Because in AdaBoost, once I train the classifier on # of estimator=50, as I move along to train # of estimator=100, the first 50 classifiers and their weights don't change. I wonder if there is a way to start training directly with the 51st weak learner in this case. </p>