Thanks for your message. In our use case, we want to perform learning to rank and train a decision tree using BM25 scores as one of our features. Decision trees requires normalised features to be able to properly split the data. Since BM25 scores for different queries varies considerably, decision tree cannot find a suitable threshold to split.
What was the normalisation in Lucene 6? We are using Lucene 6.4.2 but could not find any way to normalise BM25 scores other than hacking into the code. -- View this message in context: http://lucene.472066.n3.nabble.com/Is-it-possible-to-normalise-BM25-scores-in-the-query-level-tp4342991p4343048.html Sent from the Lucene - Java Users mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: java-user-unsubscr...@lucene.apache.org For additional commands, e-mail: java-user-h...@lucene.apache.org
