predictive modeling

XGBoost is a fantastic open source implementation of Gradient Boosting Machines, a general purpose supervised learning method that achieves the highest accuracy on a wide range of datasets in practical applications. Deep learning is all the hype now, but apart from specific domains such as images, speech or text (i.e. problems with higher-level abstractions to be learnt and/or perception problems, where deep learning achieved some remarkable results indeed), it is usually outperformed by Gradient Boosting in a majority of general business domains and supervised learning applications. Proof of this and also because XGBoost has an easy-to-use interface from both R and Python, XGBoost has become a favorite tool in Kaggle competitions. Besides feature engineering, cross-validation and ensembling, XGBoost is a key ingredient for achieving the highest accuracy in many data science competitions and more importantly in practical applications.

XGBoost is a fantastic open source implementation of Gradient Boosting Machines, one of the most accurate general purpose supervised learning algorithms. With interfaces from both R and Python among others, it has become a favorite tool in Kaggle competitions, and it’s also widely used in real-world applications. In this meetup we’ll have a talk from the best person on the topic,Tianqi Chen, main author of XGBoost.

With so much hype about “big data” and the industry pushing for “big data” analytical tools for everyone, the question arises how many people have big data (for analytics) and how many of them really need these tools (which are more complex and often more immature compared to the traditional tools for analytics).

This is a re-publication of a blog post from a blog I created not long before we got the idea to start DataScience.LA, and in many aspects it is the genesis of DSLA (the blog’s name, the people getting together - thanks Eduardo, Amelia and Leigh). It was also the spark that lead to the creation of the Python Data Science LA meetup.

The Interview # In the video above, Max provides some amazing insights into the why and how of caret, an R package he created. He also discusses his book on Applied Predictive Modeling which he co-authored with Kjell Johnson, including details on how he set out to write the book he wished he would have had. As a special bonus, Max also describes Quinlan’s C5.0, an alternate “forest of decision trees” algorithm, the secrets of which were hidden behind commercial licensing for years - and which has recently been ported and made available to the R ecosystem. Whether you are a beginner just getting your feet wet with R and predictive modeling, or a seasoned data scientist, this interview has something for everyone.