Calculating precision and recall is actually quite easy. Imagine there are 100 positive cases among 10,000 cases. You want to predict which ones are positive, and you pick 200 to have a better chance of catching many of the 100 positive cases. You record the IDs of your predictions, and when you get the actual results you sum up how many times you were right or wrong.

A common approach in positive-unlabeled learning is to train a classification model between labeled and unlabeled data. This strategy is in fact known to give an optimal classifier under mild conditions; however, it results in biased empirical estimates of the classifier performance. In this work, we show that the typically used performance measures such as the receiver operating characteristic curve, or the precision-recall curve obtained on such data can be corrected with the knowledge of class priors; i.e., the proportions of the positive and negative examples in the unlabeled data. We extend the results to a noisy setting where some of the examples labeled positive are in fact negative and show that the correction also requires the knowledge of the proportion of noisy examples in the labeled positives. Using state-of-the-art algorithms to estimate the positive class prior and the proportion of noise, we experimentally evaluate two correction approaches and demonstrate their efficacy on real-life data.

Machine learning has long powered many products we interact with daily–from "intelligent" assistants like Apple's Siri and Google Now, to recommendation engines like Amazon's that suggest new products to buy, to the ad ranking systems used by Google and Facebook. More recently, machine learning has entered the public consciousness because of advances in "deep learning"–these include AlphaGo's defeat of Go grandmaster Lee Sedol and impressive new products around image recognition and machine translation. In this series, we'll give an introduction to some powerful but generally applicable techniques in machine learning. These include deep learning but also more traditional methods that are often all the modern business needs. After reading the articles in the series, you should have the knowledge necessary to embark on concrete machine learning experiments in a variety of areas on your own.

WrestleMania 33 is still more than two months away, but the pieces for the match card are falling into place. Following the Royal Rumble, there is a much better sense of which matches will headline WWE's biggest show of 2017. WrestleMania 32 featured 12 matches and six hours of wrestling, and the pay-per-view on April 2 in Orlando should be similar. Much of the card will be determined by what happens at Elimination Chamber on Feb. 12 and Fastlane on March 5. Rumors have been circulating for weeks regarding the potential WrestleMania 33 match card, and recent events indicate that some of the reports are correct. Plans are constantly changing in WWE, though it isn't hard to guess who certain wrestlers will face on the "grandest stage of them all."

Consider the tragedy of a child killed by neglect and abuse. Now consider the tragedy of a child taken from parents who would not have criminally abused her. Computer algorithms might soon help humans make such difficult decisions -- but only if we recognize the myriad ways in which they can go wrong. In countless cities across the nation, child welfare services make extremely tough calls every day. With limited resources and information, they must often rely on gut instinct in predicting who is most vulnerable.

Machine learning has long powered many products we interact with daily–from "intelligent" assistants like Apple's Siri and Google now, to recommendation engines like Amazon's that suggest new products to buy, to the ad ranking systems used by Google and Facebook. More recently, machine learning has entered the public consciousness because of advances in "deep learning"–these include AlphaGo's defeat of Go grandmaster Lee Sedol and impressive new products around image recognition and machine translation. In this series, we'll give an introduction to some powerful but generally applicable techniques in machine learning. These include deep learning but also more traditional methods that are often all the modern business needs. After reading the articles in the series, you should have the knowledge necessary to embark on concrete machine learning experiments in a variety of areas on your own.

The 2017 Royal Rumble is one of the most unpredictable in recent memory. A number of WWE superstars have a legitimate chance to win a spot in the main event of WrestleMania 33, and the title matches on the pay-per-view are not easy to call. The Undertaker, Braun Strowman, Finn Balor and Randy Orton are among just a few wrestlers that could win the 30-man battle royal. Both John Cena and Roman Reigns are fighting for world titles, though it wouldn't be shocking to see either AJ Styles or Kevin Owens retain the belts in those matches. With three matches on the pre-show and five more on the PPV, Sunday's event is likely to last for close to six hours.

Today's guest blogger, Toshi Takeuchi used machine learning on a job-related dataset for predictive analytics. Let's see what he learned. Companies spend money and time recruiting talent and they lose all that investment when people leave. Therefore companies can save money if they can intervene before their employees leave. Perhaps this is a sign of a robust economy, that one of the datasets popular on Kaggle deals with this issue: Human Resources Analytics - Why are our best and most experienced employees leaving prematurely?

Connectivity studies using resting-state functional magnetic resonance imaging are increasingly pooling data acquired at multiple sites. While this may allow investigators to speed up recruitment or increase sample size, multisite studies also potentially introduce systematic biases in connectivity measures across sites. In this work, we measure the inter-site effect in connectivity and its impact on our ability to detect individual and group differences. Our study was based on real, as opposed to simulated, multisite fMRI datasets collected in N=345 young, healthy subjects across 8 scanning sites with 3T scanners and heterogeneous scanning protocols, drawn from the 1000 functional connectome project. We first empirically show that typical functional networks were reliably found at the group level in all sites, and that the amplitude of the inter-site effects was small to moderate, with a Cohen's effect size below 0.5 on average across brain connections. We then implemented a series of Monte-Carlo simulations, based on real data, to evaluate the impact of the multisite effects on detection power in statistical tests comparing two groups (with and without the effect) using a general linear model, as well as on the prediction of group labels with a support-vector machine. As a reference, we also implemented the same simulations with fMRI data collected at a single site using an identical sample size. Simulations revealed that using data from heterogeneous sites only slightly decreased our ability to detect changes compared to a monosite study with the GLM, and had a greater impact on prediction accuracy. Taken together, our results support the feasibility of multisite studies in rs-fMRI provided the sample size is large enough.

We've traditionally seen machine learning interview questions pop up in several categories. The first really has to do with the algorithms and theory behind machine learning. You'll have to show an understanding of how algorithms compare with one another and how to measure their efficacy and accuracy in the right way. The second category has to do with your programming skills and your ability to execute on top of those algorithms and the theory. The third has to do with your general interest in machine learning: you'll be asked about what's going on in the industry and how you keep up with the latest machine learning trends. Finally, there are company or industry-specific questions that test your ability to take your general machine learning knowledge and turn it into actionable points to drive the bottom line forward. We've divided this guide to machine learning interview questions into the categories we mentioned above so that you can more easily get to the information you need when it comes to machine learning interview questions. These algorithms questions will test your grasp of the theory behind machine learning.