The key objectives of this repository is to learn how to train and evaluate a non-trivial machine learning model. More specifically, the task is called “learning-to-rank”. It consists of a set of training features that contain the relevance labels 0 (not relevant), 1 (partially relevant) and 2 (relevant), for a large set of query-document pairs. The task is to research this problem and demonstrate a suitable solution, train a model, and produce a result file from a training set that will be scored using standard evaluation measures in Information Retrieval. If you are unfamiliar with IR, you might want to look through the book '''Learning to Rank for Information Retrieval by Tie-Yan Liu''' , which is readily available online
- A2.py : This contains the python script demonstrating the training and ranking model
- A2.run : This is a text file containing the run results of the A2.py on the dataset
- requirements.txt : A text file containing the library and tools requirements of the program.