Random Forests In R

Anish Sing Walia explains the basics of random forests and provides sample code in R:

Random Forests are similar to a famous Ensemble technique called Bagging but have a different tweak in it. In Random Forests the idea is to decorrelate the several trees which are generated on the different bootstrapped samples from training Data.And then we simply reduce the Variance in the Trees by averaging them.
Averaging the Trees helps us to reduce the variance and also improve the Perfomance of Decision Trees on Test Set and eventually avoid Overfitting.

The idea is to build lots of Trees in such a way to make the Correlation between the Trees smaller.

Random forests frequently give a good answer to classification problems, enough so as to make them a nice starting point.

Related Posts

The Lesser-Known Apply Functions In R

Andrew Treadway covers a few of the lesser-known apply functions in R: rapply Let’s start with rapply. This function has a couple of different purposes. One is to recursively apply a function to a list. We’ll get to that in a moment. The other use of rapply is to a apply a function to only those elements in […]

Read More

Bias Correction In Standard Deviation Estimates

John Mount explains how to perform bias correction and explains why it happens so rarely in practice: The bias in question is falling off at a rate of 1/n (where n is our sample size). So the bias issue loses what little gravity it ever may have ever had when working with big data. Most sources of noise will […]

Read More

Categories

July 2017
MTWTFSS
« Jun Aug »
 12
3456789
10111213141516
17181920212223
24252627282930
31