By Teja Kodali
Hello everyone! In this article I will show you how to run the random forest algorithm in R. We will use the wine quality data set (white) from the UCI Machine Learning Repository.
What is the Random Forest Algorithm?
In a previous post, I outlined how to build decision trees in R. While decision trees are easy to interpret, they tend to be rather simplistic and are often outperformed by other algorithms. Random Forests are one way to improve the performance of decision trees. The algorithm starts by building out trees similar to the way a normal decision tree algorithm works. However, every time a split has to made, it uses only a small random subset of features to make the split instead of the full set of features (usually (sqrt[]{p}), where p is the number of predictors). It builds multiple trees using the same process, and then takes the average of all the trees to arrive at the final model. This works by reducing the amount of correlation between trees, and thus helping reduce the variance of the final tree. The simplest way to understand this is (as explained in Introduction to Statistical Learning): if you have some numbers (Z_1, Z_2,…,Z_n) with a variance of (sigma^2), then their mean (overline{Z}) will have variance (sigma^2/n).
Exploring Data Analysis
Let us read in the data and explore it. We can read in the data directly from the page using the read.table function.
url
fixed.acidity volatile.acidity citric.acid residual.sugar chlorides free.sulfur.dioxide total.sulfur.dioxide density pH sulphates alcohol quality
1 7.0 0.27 0.36 20.7 ...read moreSource:: r-bloggers.com