| # Purpose: Evaluate your classification model against some baseline metrics | |
| # Establish that your model is not just making lucky guesses but is significantly better than a random model | |
| # Author : Shaheen Gauher - Data Scientist at Microsoft | |
| # Note: The code below requires MRS (Microsoft R Server, formally Revolution R Enterprise (RRE)) | |
| # http://blog.revolutionanalytics.com/2016/01/microsoft-r-open.html | |
| # MRS can be downloaded from https://www.dreamspark.com/Product/Product.aspx?productid=105 | |
| #download data from | |
| #https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data |
| This gist includes components of a oozie workflow - scripts/code, sample data | |
| and commands; Oozie actions covered: shell action, email action | |
| Action 1: The shell action executes a shell script that does a line count for files in a | |
| glob provided, and writes the line count to standard output | |
| Action 2: The email action emails the output of action 1 | |
| Pictorial overview of job: | |
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The pricing should be ~3$ for the first year (assuming 30GB upload per month). See here for more details.