By Atallah, Raskin, Hempelmann, Karahan, Sion, Topkara, Triezenberg
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Extra info for Natural Language Watermarking and Tamperproofing
Sample text
What we’re going to do is we’re going to go through each of the variables in the data set and try to fit a generalize linear model, in this case a logistic regression, to see if we can predict if an email is spam or not by using just a single variable. Here, using the reformulate function to create a formula that includes the response, which is just the type of email and one of the variables of the data set, and we’re just going to cycle through all the variables in this data set Structure of a Data Analysis: Part 2 44 using this for-loop to build a logistic regression model, and then subsequently calculate the cross validated error rate of predicting spam emails from a single variable.
And a slightly more concrete version of this question that can be used to translate into a Cisco problem was, can I use quantitative characteristics of the emails to classify them as SPAM or HAM? 33 Structure of a Data Analysis: Part 2 34 Splitting the Dataset Our data set, again, was f=rom this UCI Machine Learning Repository, which had already been cleaned up, and it was available in the current lab package as a data set. This data set had 4,600 observations or emails that had been kind of characterized along 58 different variables.
The R Markdown to Markdown to HTML work flow can be easily managed using R Studio25 (but is not required). Lastly, R Markdown documents have the benefit that they are purely textual documents that can play well with version control systems that are primarily designed to track changes in text files. Markdown Syntax Markdown has a very simple syntax that we will briefly review here. * _so will this text_ This text will appear italicized! ** __as will this text__ This text will appear bold! as will this text Unordered Lists - first item in list - second item in list - third item in list • first item in list • second item in list • third item in list Ordered Lists 1.
Natural Language Watermarking and Tamperproofing by Atallah, Raskin, Hempelmann, Karahan, Sion, Topkara, Triezenberg
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