When a clogged artery landed Peter Szolovits in the hospital for a coronary bypass operation in mid-October, he noticed a few incongruities other patients might not have. Machines that performed intertwined functions--dosing and delivering medication, for example--did not communicate with one another, and patient statistics detailed on paper were not in the hospital's electronic medical records. As head of the Massachusetts Institute of Technology's Clinical Decision Making Group, which works to apply artificial intelligence (AI) to medicine, Szolovits knew that intelligent systems could optimize care by working together better to eliminate errors as well as avoid repetition of medical tests. Indeed, in the midst of the U.S. health care debate, some experts say that AI could lift some of the burden on physicians by helping them diagnose conditions and choose treatments. Of course, the same claim echoed in the 1970s and 1980s, when a media blitz put medical AI on the cover of newsweeklies.

The London Ambulance Service is investigating whether computer failure in the early hours of New Year's Day may have contributed to the death of a patient. BBC News can reveal that at least one 999 patient died during the period that the computers were down. A major investigation is being carried out to determine the cause of the problems and the full clinical impact. The Care Quality Commission said it would inspect the trust next month. The computer-aided dispatch system, which logs emergencies and allocates ambulances, failed just after midnight.

In this article we will discuss the machine learning method called "decision trees", moving quickly over the usual "how decision trees work" and spending time on "why decision trees work." We will write from a computational learning theory perspective, and hope this helps make both decision trees and computational learning theory more comprehensible. The goal of this article is to set up terminology so we can state in one or two sentences why decision trees tend to work well in practice. Newcomers to data science are often disappointed to learn that the job of the data scientist isn't tweaking and inventing new machine learning algorithms. In the "big data" world supervised learning has been a solved problem since at least 1951 (see [FixHodges1951] for neighborhood density methods, see [GordonOlshen1978] for k-nearest neighbor and decision tree methods).

More than six billion dollars: That's how much health care providers and consumers will be spending every year on artificial intelligence tools by 2021--a tenfold increase from today--according to a new report from research firm Frost & Sullivan. AI will be everywhere--from diagnosing cancer to providing weight-loss coaching, says Venkat Rajan, who has the great title of global director for the company's Visionary Healthcare Program. "Prior to 2015, most of what was happening was sort of academic: pilot programs, exploratory, proof of concept-type stuff," he says. AI's ability to sort through scads of information, and remember everything it has ever seen, could enable a digital (and congenial) version of Dr. House, the brilliant diagnostician from the eponymous TV show, says Rajan. "At first, it's a complete mystery, it could be one of ten different things," he says, about the process in the show, and real life, called differential diagnosis. "And then he's able to sort through various issues, you know, illuminate certain factors on why it's not one of these other conditions, and he's able to pull something from memory that figures out ultimately what it is, and they can provide the appropriate treatment." Robots won't steal doctors' jobs, says Rajan, but they will spare overworked docs some of the dangerous fatigue that can lead to mistakes.

Decision tree (and its extensions such as Gradient Boosting Decision Trees and Random Forest) is a widely used machine learning algorithm, due to its practical effectiveness and model interpretability. With the emergence of big data, there is an increasing need to parallelize the training process of decision tree. However, most existing attempts along this line suffer from high communication costs. In this paper, we propose a new algorithm, called \emph{Parallel Voting Decision Tree (PV-Tree)}, to tackle this challenge. After partitioning the training data onto a number of (e.g., $M$) machines, this algorithm performs both local voting and global voting in each iteration. For local voting, the top-$k$ attributes are selected from each machine according to its local data. Then, the indices of these top attributes are aggregated by a server, and the globally top-$2k$ attributes are determined by a majority voting among these local candidates. Finally, the full-grained histograms of the globally top-$2k$ attributes are collected from local machines in order to identify the best (most informative) attribute and its split point. PV-Tree can achieve a very low communication cost (independent of the total number of attributes) and thus can scale out very well. Furthermore, theoretical analysis shows that this algorithm can learn a near optimal decision tree, since it can find the best attribute with a large probability. Our experiments on real-world datasets show that PV-Tree significantly outperforms the existing parallel decision tree algorithms in the tradeoff between accuracy and efficiency.

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Google has announced that it is now rolling out better filtering systems to counter fraudulent app rankings on the Play Store. The company says that these are violating the Google Play Developer Policy and will bring harm to the Android community. "From time to time, we observe instances of developers attempting to manipulate the placement of their apps through illegitimate means like fraudulent installs, fake reviews, and incentivized ratings," Google said on the Android Developers Blog. "Today we are rolling out improved detection and filtering systems to combat such manipulation attempts." Apps of developers who keep on using these methods may also be taken down from the Play Store completely.

This post is a summary of 3 different posts about outlier detection methods. One of the challenges in data analysis in general and predictive modeling in particular is dealing with outliers. There are many modeling techniques which are resistant to outliers or reduce the impact of them, but still detecting outliers and understanding them can lead to interesting findings. We generally define outliers as samples that are exceptionally far from the mainstream of data.There is no rigid mathematical definition of what constitutes an outlier; determining whether or not an observation is an outlier is ultimately a subjective exercise. There are several approaches for detecting Outliers.

This is a guest repost by Jacob Joseph from CleverTap. Why would you want to convert a numerical variable into categorical one? Depending on the situation, it can lead to a better interpretation of the numerical variable, quick segmentation or just an additional feature for building your predictive model by creating bins for the numerical variable. Binning is a popular feature engineering technique. Suppose your hypothesis is that the age of a customer is correlated with their tendency to interact with a mobile app.

In this work we establish and investigate connections between causes for query answers in databases, database repairs wrt. denial constraints, and consistency-based diagnosis. The first two are relatively new research areas in databases, and the third one is an established subject in knowledge representation. We show how to obtain database repairs from causes, and the other way around. Causality problems are formulated as diagnosis problems, and the diagnoses provide causes and their responsibilities. The vast body of research on database repairs can be applied to the newer problems of computing actual causes for query answers and their responsibilities. These connections, which are interesting per se, allow us, after a transition -inspired by consistency-based diagnosis- to computational problems on hitting sets and vertex covers in hypergraphs, to obtain several new algorithmic and complexity results for database causality.