I am embarking on some modelling of a large data set, with the goal to create a predictive model using R, selecting predictors analytically from an abundance: ~1000 observations of ~200 predictors. I will be looking at various methods, for example boosting.
All the methods I am looking at require fine-tuning of model parameters and I would like to optimise this process as far as possible - both in terms of effectiveness and computational cost.
In this video on gradient boosting - which is all in Python - I saw
a function in the
scikit-learn module in python called
GridSearchCV that accepts a range for all model parameters and works through all of them to find the optimal set of parameters specific to your model, based on how you define optimal.
Is the established way of achieving this in R to use the caret package and
train methodology? This seems very comparable to the Python method linked above, but are there different ways? Literature references?