Gradient Boosting In R

Anish Sing Walia walks us through a gradient boosting exercise using R:

An important thing to remember in boosting is that the base learner which is being boosted should not be a complex and complicated learner which has high variance for e.g a neural network with lots of nodes and high weight values.For such learners boosting will have inverse effects.

So I will explain Boosting with respect to decision trees in this tutorial because they can be regarded as weak learners most of the times.We will generate a gradient boosting model.

Click through for more details.  H/T R-Bloggers

Related Posts

The Intuition Behind Principal Component Analysis

Holger von Jouanne-Diedrich gives us an intuition behind how principal component analysis (PCA) works: Principal component analysis (PCA) is a dimension-reduction method that can be used to reduce a large set of (often correlated) variables into a smaller set of (uncorrelated) variables, called principal components, which still contain most of the information.PCA is a concept […]

Read More

Plotting Diagrams In R With nest() And map()

Sebastian Sauer shows how to display multiple ggplot2 diagrams together using facets as well as a combination of the nest() and map() functions: One simple way is to plot several facets according to the grouping variable: d %>% ggplot() + aes(x = hp, y = mpg) + geom_point() + facet_wrap(~ cyl) Faceting is great, but it’s good to know […]

Read More


August 2017
« Jul Sep »