Machine Learning and Artificial Intelligence are currently driving innovation in the field of Computer Science and they are being applied on a multitude of fields across disciplines. However, traditional ML models can be still be broadly categorized into solutions of two types of problems.

  1. Classification — Which aims at labelling a particular instance of data into buckets, depending on various features.
  2. Regression — Where we desire to get a continuous real number as the output for a given feature set.

One relatively lessexplored application of Machine Learning is the ordering of data by its relevance, which becomes useful in Information Retrieval systems like search engines. These types of models focus more on the relative ordering of items rather than the individual label (classification) or score (regression), and are categorized as Learning To Rank models.

#learning-to-rank #machine-learning #data-science #neural-networks

Learning to Rank for Information Retrieval: A Deep Dive into RankNet.
1.40 GEEK