Predicting Student Employability Through the Internship Context Using Gradient Boosting Models

Predicting the Popularity of Online News using Gradient Boosting Machine

Popularity prediction of online news aims to predict the future popularity of news article prior to its publication estimating the number of shares, likes, and comments. Yet, popularity prediction is a challenging task due to various issues including difficulty to measure the quality of content and relevance of content to users; prediction difficulty of complex online interactions and informati...

Predicting Extraction Performance using Context Language Models

Exploiting lexical and semantic relationships in text can dramatically improve information retrieval accuracy. Most notably, named entities and relations between entities are crucial for effective question answering and other information retrieval tasks. Unfortunately, the success in extracting these relationships can vary for different domains and document collections. Predicting extraction pe...

Predicting Customer Churn: Extreme Gradient Boosting with Temporal Data

Accurately predicting customer churn using large scale time-series data is a common problem facing many business domains. The creation of model features across various time windows for training and testing can be particularly challenging due to temporal issues common to time-series data. In this paper, we will explore the application of extreme gradient boosting (XGBoost) on a customer dataset ...

Bioactive Molecule Prediction Using Extreme Gradient Boosting.

Following the explosive growth in chemical and biological data, the shift from traditional methods of drug discovery to computer-aided means has made data mining and machine learning methods integral parts of today's drug discovery process. In this paper, extreme gradient boosting (Xgboost), which is an ensemble of Classification and Regression Tree (CART) and a variant of the Gradient Boosting...

Phone duration modeling using gradient tree boosting