Contents
What do you need to know about ranking models?
A ranking model is a list of one or more rank stages that contain a set of rank features. The ranking model defines how the search engine calculates the relevance rank using various factors, which are represented in the ranking model as rank features.
How does the ranking model calculate the relevance rank?
The ranking model defines how the search engine calculates the relevance rank using various factors, which are represented in the ranking model as rank features. Factors used to calculate the relevance rank include, but are not limited to, the following:
How does the ranking model work in SharePoint?
When you sort search results by rank score, SharePoint places the most relevant results on top in the search result set. A search result is relevant if it receives a high rank score, which is a specific numeric score calculated by the search engine using a ranking model.
Which is an example of a two stage ranking model?
The SharePoint default ranking model is an example of two-stage ranking model. In this model, the second stage works with the top 1000 items with the highest rank score that result from the first stage. When the ranking process in the first stage is complete, the search engine re-sorts all of the items,…
How to calculate the feature importance of a model?
In this tutorial, we will look at three main types of more advanced feature importance; they are: 1 Feature importance from model coefficients. 2 Feature importance from decision trees. 3 Feature importance from permutation testing.
How to select features to create a model?
One common approach to select features to create a model is to select the top-N from a rank. Scikit-Learn, for instance, has SelectPercentile and SelectKBest that select features according to a percentile of the highest scores and according to the k highest scores, respectively. Scores, or weights, is a measure of importance.