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

Setting Up SparklyR In Azure

David Smith shows how you can spin up a Spark cluster in Azure and install SparklyR on top of it: The SparklyR package from RStudio provides a high-level interface to Spark from R. This means you can create R objects that point to data frames stored in the Spark cluster and apply some familiar R paradigms (like dplyr) […]

Read More

Zippy Base R

John Mount defends the honor of base R: The graph summarizes the performance of four solutions to the “scoring logistic regression by hand” problem: Optimized Base R: a specialized “pre allocate and work with vectorized indices” method. This is fast as it is able to express our particular task in a small number of purely […]

Read More

Categories

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