Can you be more specific about the types of data you are looking at? This will in part determine what type of algorithm will converge the fastest.
I'm also not sure how to compare methods like boosting and DL, as boosting is really just a collection of methods. What other algorithms are you using with the boosting?
In general, DL techniques can be described as layers of encoder/decoders. Unsupervised pre-training works by first pre-training each layer by encoding the signal, decoding the signal, then measuring the reconstruction error. Tuning can then be used to get better performance (e.g. if you use denoising stacked-autoencoders you can use back-propagation).
One good starting point for DL theory is:
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.73.795&rep=rep1&type=pdf
as well as these:
http://portal.acm.org/citation.cfm?id=1756025
(sorry, had to delete last link due to SPAM filtration system)
I didn't include any information on RBMs, but they are closely related (though personally a little more difficult to understand at first).
If you have metric responses, there is RE-EM tree by Sela and Simonoff (Machine Learning, 86, 169-207). The R package is called REEMtree
. It is intended for panel data with random effects, but you should be able to use it for other hierarchically nested/multilevel data as well.
If you are fine with including the domain expertise in a fixed effect model, you can also use model-based recursive partioning with the party::mob
function.
Best Answer
When you have panel data, there are a different tasks that you can try to solve, e.g. time series classification/regression or panel forecasting. And for each task, there are numerous approaches to solve it.
When you want to use machine learning methods to solve panel forecasting, there are a number of approaches:
Regarding your input data (X), treating units (e.g. countries, individuals, etc) as i.i.d. samples, you can
Regarding your output data (y), if you want to forecast multiple time points in the future, you can
All of the approaches above basically reduce the panel forecasting problem to a time series regression or tabular regression problem. Once your data is in the time series or tabular regression format, you can also append any time-invariant features for users.
Of course there are other options to solve the panel forecasting problem, like for example using classical forecasting methods like ARIMA adapted to panel data or deep learning methods that allow you to directly make sequence to sequence predictions.