As of the beginning of January 2020, it's my great pleasure to join The Machine Learning Times as editor in chief! I've taken over the main editorial duties from Eric Siegel, who founded the ML Times (also the founder of the Predictive Analytics World conference series). As you've likely noticed, we've renamed to The Machine Learning Times what until recently was The Predictive Analytics Times. In addition to a new, shiny name, this rebranding corresponds with new efforts to expand and intensify our breadth of coverage. One particular area of focus will be to increase our coverage of deep learning.

I mean, you really suck. That's what I wanted to write, but a new feature in the Office 365 version of Word called Editor made an interesting suggestion. The "machine" noted how the word "really" is superfluous, and it's true. The extra word doesn't add anything to the sentence, so I removed it. I've been writing professionally since 2001 (around 10,000 published articles now), but I'm still learning, I guess.

I mean, you really suck. That's what I wanted to write, but a new feature in the Office 365 version of Word called Editor made an interesting suggestion. The "machine" noted how the word "really" is superfluous, and it's true. The extra word doesn't add anything to the sentence, so I removed it. I've been writing professionally since 2001 (around 10,000 published articles now), but I'm still learning, I guess.

The Wikimedia Foundation has recently observed that newly joining editors on Wikipedia are increasingly failing to integrate into the Wikipedia editors' community, i.e. the community is becoming increasingly harder to penetrate [1]. To sustain healthy growth of the community, the Wikimedia Foundation aims to quantitatively understand the factors that determine the editing behavior, and explain why most new editors become inactive soon after joining. As a step towards this broader goal, the Wikimedia foundation sponsored the ICDM (IEEE International Conference for Data Mining) contest [2] for the year 2011. The objective for the participants was to develop models to predict the number of edits that an editor will make in future five months based on the editing history of the editor. Here we describe the approach we followed for developing predictive models towards this goal, the results that we obtained and the modeling insights that we gained from this exercise. In addition, towards the broader goal of Wikimedia Foundation, we also summarize the factors that emerged during our model building exercise as powerful predictors of future editing activity.