Ensemble Architecture (Mark Peng)_2Figure 1. Ensemble Architecture

Got 7th place of 1334 teams in Kaggle Crowdflower Search Results Relevance Contest! This contest asks kagglers to predict the relevance score of search results made by the users using machine learning models.

In this post I would like to share some of our findings and experience in NLP, feature extraction and feature selection tasks, and also my ensemble architecture. Feature engineering helped us a lot in improving the performance of our different models.

Due to the limitation of Web editor, I cannot show my post in well format here. So please check the following link to download and see full post:

https://drive.google.com/file/d/0B-20iuF5gIcyQ2FldEp6UnlyVG8/view?usp=sharing
Hope it can help other kagglers to some extent. 🙂

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