Number of topics, citation flow, facebook shares, linkedin shares and google shares . We've applied a standard scalar (multiplier) to these features to center them C Level Executive List around the mean, but other than that they don't require any additional pre-processing. A categorical variable is a variable that can take a limited number of values, each value representing a different group or category. The categorical variables we used include the most frequent keywords, C Level Executive List as well as locations and organizations throughout the site, in addition to topics the website is trusted for. Preprocessing of these features included transforming them into digital labels and subsequent hot coding. Text elements are obviously composed of text. They include search term, website
Content, title, meta description, anchor text, headings (h3, h2, h1) and others. It is important to point out that there is no clear cut difference between some categorical attributes (e.G. Organizations mentioned on the site) and the text, and some attributes are indeed passed from one category to another in different models. Feature engineering we designed additional features, which C Level Executive List correlate with rank. Most of these features are boolean (true or false), but C Level Executive List some are numeric. An example of a boolean feature is the exact search term included in the website text, while a numeric feature is the number of search term tokens
Included in the website text. Here are some of the features we have designed. Image showing boolean and quantitative features that were engineered run tf-idf to pre-process the text features, we used the tf-idf (term-frequency, inverse document frequency) algorithm. C Level Executive List This algorithm considers each instance as a document and all instances as a corpus. Then it assigns a score to each term, where the more frequent the term is in the document and the less C Level Executive List it is in the corpus, the higher the score. We tried two tf-idf approaches, with slightly different results depending on the model. The first approach was to concatenate all