Maybe I'm missing something, I can't figure it, I need your help.

I'm training an xgb model using these lines 

```
 XGB = xgb.XGBClassifier(objective ='multi:softprob',
                        learning_rate = 0.3,
                        max_depth = 1, 
                        n_estimators = 3,
                        n_jobs = 5)
clf = XGB.fit(X_train, Y_train) 

```

When I print the XGB model it says that I have indeed trained 3 trees :

```
XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
              colsample_bynode=1, colsample_bytree=1, gamma=0,
              learning_rate=0.3, max_delta_step=0, max_depth=1,
              min_child_weight=1, missing=None, n_estimators=3, n_jobs=5,
              nthread=None, objective='multi:softprob', random_state=0,
              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
              silent=None, subsample=1, verbosity=1)  

```

But when I run this line to see where features were split 

```
dump_list = clf.get_booster().get_dump()

```

I get 9 lines

```
['0:[f0<0.942677855] yes=1,no=2,missing=1\n\t1:leaf=-0.103566267\n\t2:leaf=0.43779847\n',
 '0:[f0<0.954393268] yes=1,no=2,missing=1\n\t1:leaf=0.200365365\n\t2:leaf=-0.216199294\n',
 '0:[f13<0.651464462] yes=1,no=2,missing=1\n\t1:leaf=0.276390254\n\t2:leaf=-0.219127133\n',
 '0:[f0<0.917573214] yes=1,no=2,missing=1\n\t1:leaf=-0.110939182\n\t2:leaf=0.292450339\n',
 '0:[f0<0.966108799] yes=1,no=2,missing=1\n\t1:leaf=0.135595635\n\t2:leaf=-0.194633663\n',
 '0:[f11<0.6690377] yes=1,no=2,missing=1\n\t1:leaf=0.202725366\n\t2:leaf=-0.196870551\n',
 '0:[f0<0.899163187] yes=1,no=2,missing=1\n\t1:leaf=-0.107093893\n\t2:leaf=0.230380043\n',
 '0:[f0<0.974476993] yes=1,no=2,missing=1\n\t1:leaf=0.10007298\n\t2:leaf=-0.180789232\n',
 '0:[f13<0.588702917] yes=1,no=2,missing=1\n\t1:leaf=0.235898077\n\t2:leaf=-0.177840069\n']

```

Does that mean that 9 trees were fitted?

I've noticed that the number of lines I get here is correlated with the number of classes in my dataset. Here I'm using a dataset with 3 classes. When I use a dataset with 2 classes I get 6 lines. This hints to a relationship between the number of classes and the number of fitted trees which doesn't really make sense. So my other question is what is the interpretation of the output of clf.get\_booster().get\_dump().

Thank you.