Examples for the R/finance 2017 conference

See the main Syberia repo when you feel ready or check out the website.

This repository holds some examples and slides for the R/finance 2017 conference presentation on Syberia.

It is organized as follows:

• The project itself is a Syberia project. You can run the example model by opening R from the root of this project and executing run("example"). Notice that following this with model$predict(iris[1:5, ]) gives you some predictions.
• The Syberia talk slides (or PPT format).

Check out the MicrosoftR engine that we built in 15 minutes after David Smith's talk. You can also see some scratch notes.

Execution

Open R from the root of this project.

# Running the titanic example.
run("titanic", to = 1)
data <- within(A, X <- NULL)
run("titanic")
titanic$predict(data)

# Running the survey example
run("survey")

Project details

(Copy pasted from another repo so slightly incorrect.)

This repository gives a full example of how to use Syberia for a simple structured supervised learning project. It consists of the following files:

• The lockfile - A specification of which packages should be loaded for an R session using this project through lockbox.
• The engines file - This file is necessary for any Syberia project. In this example, we are using the modeling engine.
• An example model - A trivial model showing how to create an lm classifier with some trivial feature engineering. After running this model, the final output will be in a global variable called model.
• An example mungebit - A trivial mungebit, the Syberia approach to feature engineering that allows re-use of the same code during experimental sandbox training and real-time prediction.

To run the example model, open an R console from the root of this project and type run("example1"). (All dependencies should auto-install.) This uses fuzzy matching, so you can run the model by leaving out letters as long as they appear consecutively in the model filename: run("ex1") or run("mple") will work equally well.