Category: Data Science

Michael Mayer has a new package:

The current version offers:

• H statistics per feature, feature pair, and feature triple
• multivariate predictions at no additional cost
• a convenient API
• other important tools from explainable ML:
  • performance calculations
  • permutation importance (e.g., to select features for calculating H-statistics)
  • partial dependence plots (including grouping, multivariate, multivariable)
  • individual conditional expectations (ICE)
• Case-weights are available for all methods, which is important, e.g., in insurance applications.

Click through for an example of how it works, followed by some simple benchmarking to give you an idea of how it performs compared to similar tools.

Steven Sanderson builds a tree:

Decision trees are a powerful machine learning algorithm that can be used for both classification and regression tasks. They are easy to understand and interpret, and they can be used to build complex models without the need for feature engineering.

Once you have trained a decision tree model, you can use it to make predictions on new data. However, it can also be helpful to plot the decision tree to better understand how it works and to identify any potential problems.

In this blog post, we will show you how to plot decision trees in R using the rpart and rpart.plot packages. We will also provide an extensive example using the iris data set and explain the code blocks in simple to use terms.

Read on to see an example of how to do this.

Steven Sanderson doesn’t need all of those data points:

Data visualization is a powerful tool for gaining insights from your data. In R, you have a plethora of libraries and functions at your disposal to create stunning and informative plots. One common task is to plot a subset of your data, which allows you to focus on specific aspects or trends within your dataset. In this blog post, we’ll explore various techniques to plot subsets of data in R, and I’ll explain each step in simple terms. Don’t worry if you’re new to R – by the end of this post, you’ll be equipped to create customized plots with ease!

Click through for several techniques for subsetting data, as well as reasons why you might want to do it.

Adrian Tam tries out a couple of tests:

R as a data analytics platform is expected to have a lot of support for various statistical tests. In this post, you are going to see how you can run statistical tests using the built-in functions in R. Specifically, you are going to learn:

• What is t-test and how to do it in R
• What is F-test and how to do it in R

This is one of the things that R does best among any language: statistical testing. R has support for an enormous number of statistical functions, either built into the base language or available as packages.