```python model = RandomBoostingRegressor() rb = GridSearchCV(model, param_grid={'n_estimators':[75, 100, 150]}, cv=5) rb = rb.fit(X_train, y_train) ``` This code fails with an **IndexError** ``` File ".../python_random_boost/random_boost/random_boost.py", line 1183, in _fit_stage self.depths_[i] = depth IndexError: index 100 is out of bounds for axis 0 with size 100 ``` The reason is that RandomBoostingRegressor() is initialized with `n_estimators=100`, and it seems GridSearchCV() is not able to properly overwrite `n_estimators` when searching the grid (hence leading to an error when it tries out `n_estimators=150`. This is not a problem with GradientBoostingRegressor(), which has the same default value for `n_estimators`. In essence, the problem is related to the fact that I create a vector in which I save the drawn tree depth values. The length of this vector equals `n_estimators` and seems to be set to 100 independent from the actual value tried out by GridSearchCV.