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

R 3.5.0 Released

Tal Galili announces that R 3.5.0 is now available: By default the (arbitrary) signs of the loadings from princomp() are chosen so the first element is non-negative. If –default-packages is not used, then Rscript now checks the environment variable R_SCRIPT_DEFAULT_PACKAGES. If this is set, then it takes precedence over R_DEFAULT_PACKAGES. If default packages are not specified on the command line or by one […]

Read More

Issues Starting ML Services

Jen Stirrup has a quick rundown of some reasons why Machine Learning Services might give you an error when you try to start it up: Msg 39023, Level 16, State 1, Procedure sp_execute_external_script, Line 1 [Batch Start Line 3] ‘sp_execute_external_script’ is disabled on this instance of SQL Server. Use sp_configure ‘external scripts enabled’ to enable […]

Read More


July 2017
« Jun Aug »