by Mike Bowles and Jeremy Howard
When doing predictive modelling, there are two situations in which you might find yourself:
You need to fit a well-defined parameterised model to your data, so you require a learning algorithm which can find those parameters on a large data set without over-fitting
You just need a “black box” which can predict your dependent variable as accurately as possible, so you need a learning algorithm which can automatically identify the structure, interactions, and relationships in the data
For case (1), lasso and elastic-net regularized generalized linear models are a set of modern algorithms which meet all these needs. They are fast, work on huge data sets, and avoid over-fitting automatically. They are available in the “glmnet” package in R.
For case (2), ensembles of decision trees (often known as “Random Forests”) have been the most successful general-purpose algorithm in modern times. For instance, most Kaggle competitions have at least one top entry that heavily uses this approach. This algorithm is very simple to understand, and is fast and easy to apply. It is available in the “randomForest” package in R.
Mike and Jeremy will explain in simple terms, using no complex math, how these algorithms work, and will also explain using numerous examples how to apply them using R. They will also provide advice on how to select from these algorithms, and will show how to prepare the data, and how to use the trained models in practice.
United States United States, Santa Clara
28th February to 1st March 2012