I’ve worked with various alternate file handlers in python before and wanted to explore the options in R. I was pleasantly surprised to find handlers prebuilt for tasks like compressing data. In addition, a pipe function is available to allow you to use less common commands on your file, like gpg for encryption.
I put together a quick video demo of how to use these functions, and it’s available on youtube:
The mean of means (of state e) is close to .36. If you take .3 * .36 + .4 * (1-.36), you get .364, so this seems to make sense. Note that I’m weighting the switching to e percentage based on the percentage of being in that state in the first place.
I’ve been creating a video series on machine learning in R. Two videos are up and there is a third on the way.
The first video series is a Getting Started series that looks at predicting continuous values, classification, and other first steps into modeling. I start with using the algorithms directly, and finish with the caret package. It’s available here.
A screen capture of the course
The second video series is picks up on more advanced algorithms and techniques, for example random forests, support vector machines, clustering and text processing. I tried to focus on fairly serious data sets that might resemble the type you would use in the real world. The second video series is available here.
Note that I’m using the tm package, which is the traditional way to work with a document collection in R. There are new ways like tidytext that are gaining popularity. I may do a follow up talk on that.
The Monty Hall Problem is famous in the world of statistics and probability. For those struggling with the intuition, simulating the problem is a great way to get at the answer. Randomly choose a door for the prize, randomly choose a door for the user to pick first, play out Monty’s role as host, and then show the results of both strategies.
Simulating the strategies of Monty Hall
The numeric output will vary, but look something like: