Subscribe to DSC Newsletter

Diego Marinho de Oliveira's Blog (7)

Deep Learning with Professor Geoff Hinton

This video session features the keynote speaker Professor Geoff Hinton FRS, “Deep Learning”. This lecture was filmed on May 22, 2015.

Watch full video at …

Continue

Added by Diego Marinho de Oliveira on April 4, 2016 at 5:32am — No Comments

XGBoost: A Scalable Tree Boosting System

"Abstract Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and…

Continue

Added by Diego Marinho de Oliveira on March 14, 2016 at 7:02am — 1 Comment

AirBnB New User Bookings, Kaggle Winner's Interview: 3rd Place

AirBnB New User Bookings was a popular recruiting competition that challenged Kagglers to predict the first country where a new user would book travel. This was the first recruiting competition on Kaggle with scripts enabled. AirBnB…

Continue

Added by Diego Marinho de Oliveira on March 10, 2016 at 2:30am — No Comments

Scikit-Learn Tutorial Series



Kaggle released a series with tutorials in their blog. I recommend to anyone who is starting or want to learn more about the tool.…

Continue

Added by Diego Marinho de Oliveira on March 8, 2016 at 12:30am — No Comments

Evaluating RF for Survival Analysis Using Prediction Error Curves

Ulla B. Mogensen, Hemant Ishwaran, Thomas A. Gerds (2012). Evaluating Random Forests for Survival Analysis Using Prediction Error Curves. Journal of Statistical Software, 50(11), 1-23.



Abstract Prediction error curves are increasingly used to assess and compare predictions in survival analysis. This article surveys the R package pec which provides a set of functions for efficient computation of prediction error…
Continue

Added by Diego Marinho de Oliveira on April 10, 2015 at 12:21am — No Comments

Tuning Machine Learning Models Using the Caret R Package

Machine learning algorithms are parameterized so that they can be best adapted for a given problem. A difficulty is that configuring an algorithm for a given problem can be a project in and of itself.

Like selecting ‘the best’ algorithm for a problem you cannot know before hand which algorithm parameters will be best for a problem. The best thing to do is to investigate empirically with controlled experiments.

The caret R package was designed to make finding…

Continue

Added by Diego Marinho de Oliveira on April 7, 2015 at 6:41am — No Comments

Building an NCAA Men’s Basketball Predictive Model

Authors:  / Gregory J. Matthews.

Journal of Quantitative Analysis in Sports. Volume 11, Issue 1, Pages 5–12.



Abstract
 Computing and machine learning advancements have led to the creation of many cutting-edge predictive algorithms, some of which have been demonstrated to provide more accurate forecasts…
Continue

Added by Diego Marinho de Oliveira on April 7, 2015 at 12:46am — No Comments

Videos

  • Add Videos
  • View All

© 2019   Data Science Central ®   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service