# Tuning xgboost Models In R

2018-05-17

My favourite Boosting package is the xgboost, which will be used in all examples below. Before going to the data let’s talk about some of the parameters I believe to be the most important. These parameters mostly are used to control how much the model may fit to the data. We would like to have a fit that captures the structure of the data but only the real structure. In other words, we do not want the model to fit noise because this will be translated in a poor out-of-sample performance.

• eta: Learning (or shrinkage) parameter. It controls how much information from a new tree will be used in the Boosting. This parameter must be bigger than 0 and limited to 1. If it is close to zero we will use only a small piece of information from each new tree. If we set eta to 1 we will use all information from the new tree. Big values of eta result in a faster convergence and more over-fitting problems. Small values may need to many trees to converge.

• colsample_bylevel: Just like Random Forests, some times it is good to look only at a few variables to grow each new node in a tree. If we look at all variables the algorithm needs less trees to converge, but looking at, for example, $2/3$ of the variables may result in models more robust to over-fitting. There is a similar parameter called colsample_bytree that re-sample the variables in each new tree instead of each new node.

Read the whole thing.  H/T R-bloggers

## Combining Keras With Apache MXNet

2018-05-23

Lai Wei, et al, show how to build a neural network in Keras 2 using MXNet as the engine: Distributed training with Keras 2 and MXNet This article shows how to install Keras-MXNet and demonstrates how to train a CNN and an RNN. If you tried distributed training with other deep learning engines before, you […]

## There Is No Easy Button With Predictive Analytics

2018-05-23

Scott Mutchler dispels some myths: There are a couple of myths that I see more an more these days.  Like many myths they seem plausible on the surface but experienced data scientist know that the reality is more nuanced (and sadly requires more work). Myths: Deep learning (or Cognitive Analytics) is an easy button.  You […]