[selectron]

The explanation glosses over a few important details. Gradient boosting works by adding some small weight to the instances the model is incorrectly predicting. The amount of extra weight these instances get is a parameter that is tuned with validation - because this parameter can be 0, if you are doing correct cv gradient boosting trees is usually superior to random forests. You also do need to tune the number of trees you use in gradient boosting or else you will overfit.

Gradient boosting doesn't get nearly enough hype as compared to things like neural nets. The significant majority of winning solutions to Kaggle competitions for a non-image or text-processing dataset will use xgboost to do gradient boosting as part of the ensemble model. Furthermore, it is a really easy method to understand and use while still being state-of-the-art.