The Oklahoma Department of Corrections said Monday that crews are working to find the cause of the problem at the Oklahoma State Reformatory in Granite while two, 1,000 gallon water tanks are being used to provide water.

Decision Trees are a type of Supervised Machine Learning (that is you explain what the input is and what the corresponding output is in the training data) where the data is continuously split according to a certain parameter. The tree can be explained by two entities, namely decision nodes and leaves. The leaves are the decisions or the final outcomes. And the decision nodes are where the data is split. An example of a decision tree can be explained using above binary tree.

Simple yet effective, they are easily visualized, intuitively understood, and a great place to start when trying to understand what this artificial intelligence stuff is all about. Right now, out in the real-world, decision trees are being used to predict which customers will default on a loan, which credit card transactions are fraudulent, and which stocks are a good buy this week. This technology is already embedded all around us. Smart corporations have already been using this stuff for years, and now government is getting in on the action. As these systems become more sophisticated, and more embedded in every aspect of our daily lives, being an informed citizen means having at least a basic understanding of this stuff.

Six clinical commissioning groups (CCGs) across south east London will soon be able to access VisualDX's AI-powered visual diagnostic tool, as per terms of a deal between the NHS and the healthcare software company. The move, which focuses on enhancing diagnostic accuracy and aiding therapeutic decisions and diagnosis, marks a first for VisualDX, as it launches its first foothold into the UK's National Health Service (NHS). Art Papier, chief executive officer of VisualDX, said: "This agreement with NHS CCGs in South East London helps patients and clinicians alike to share in the decision-making process to optimise patient-centred outcomes in the pursuit of clinical excellence." According to the ECRI Institute, misdiagnoses took the top spot for safety concern among patients. While the cause for each misdiagnosis varies, VisualDX has build a library of more than 41,000 peer-reviewed medical images to help physicians, especially generalists, to make better informed diagnoses by allowing direct imagery comparison and offering diagnostic possibilities.

In this paper we consider the problem of combining multiple probabilistic causal models, provided by different experts, under the requirement that the aggregated model satisfy the criterion of counterfactual fairness. We build upon the work on causal models and fairness in machine learning, and we express the problem of combining multiple models within the framework of opinion pooling. We propose two simple algorithms, grounded in the theory of counterfactual fairness and causal judgment aggregation, that are guaranteed to generate aggregated probabilistic causal models respecting the criterion of fairness, and we compare their behaviors on a toy case study.

Boosted decision trees enjoy popularity in a variety of applications; however, for large-scale datasets, the cost of training a decision tree in each round can be prohibitively expensive. Inspired by ideas from the multi-arm bandit literature, we develop a highly efficient algorithm for computing exact greedy-optimal decision trees, outperforming the state-of-the-art Quick Boost method. We further develop a framework for deriving lower bounds on the problem that applies to a wide family of conceivable algorithms for the task (including our algorithm and Quick Boost), and we demonstrate empirically on a wide variety of data sets that our algorithm is near-optimal within this family of algorithms. We also derive a lower bound applicable to any algorithm solving the task, and we demonstrate that our algorithm empirically achieves performance close to this best-achievable lower bound.

Dealing with memory and time constraints are current challenges when learning from data streams with a massive amount of data. Many algorithms have been proposed to handle these difficulties, among them, the Very Fast Decision Tree (VFDT) algorithm. Although the VFDT has been widely used in data stream mining, in the last years, several authors have suggested modifications to increase its performance, putting aside memory concerns by proposing memory-costly solutions. Besides, most data stream mining solutions have been centred around ensembles, which combine the memory costs of their weak learners, usually VFDTs. To reduce the memory cost, keeping the predictive performance, this study proposes the Strict VFDT (SVFDT), a novel algorithm based on the VFDT. The SVFDT algorithm minimises unnecessary tree growth, substantially reducing memory usage and keeping competitive predictive performance. Moreover, since it creates much more shallow trees than VFDT, SVFDT can achieve a shorter processing time. Experiments were carried out comparing the SVFDT with the VFDT in 11 benchmark data stream datasets. This comparison assessed the trade-off between accuracy, memory, and processing time. Statistical analysis showed that the proposed algorithm obtained similar predictive performance and significantly reduced processing time and memory use. Thus, SVFDT is a suitable option for data stream mining with memory and time limitations, recommended as a weak learner in ensemble-based solutions.

Along with the increase of digital healthcare providers, the interest in diagnostic aids for remote diagnosis has increased as well. As patients write about their symptoms themselves, we have access to a type of data which previously was rarely recorded, and which has not been filtered by a healthcare professional. Knowledge of similar patients and similar symptoms is beneficial for doctors to arrive at a diagnosis. Therefore, the remote diagnostic process could be aided by presenting patient cases together with information about similar patients and their self-reported symptom descriptions. Apart from online diagnosis, such an aid could be beneficial in many healthcare settings, such as long-distance visits and knowledge gain from patient diaries. In this paper, we present the impact of aiding remote diagnosis by presenting clusters of similar symptoms, using symptom descriptions collected from a virtual visit application by the Swedish telemedicine provider KRY. Symptom descriptions were represented using the bag-of-words model and were then clustered using the k-means algorithm. An experiment was then conducted with 13 doctors, where patient cases were presented together with the most representative words of the associated cluster, to measure how their work was impacted. Results indicated that it was useful in more complicated cases, but also that future experiments will require further instructions on how the information is to be interpreted.

Structural causal models are a popular tool to describe causal relations in systems in many fields such as economy, the social sciences, and biology. In this work, we show that these models are not flexible enough in general to give a complete causal representation of equilibrium states in dynamical systems that do not have a unique stable equilibrium independent of initial conditions. We prove that our proposed generalized structural causal models do capture the essential causal semantics that characterize these systems. We illustrate the power and flexibility of this extension on a dynamical system corresponding to a basic enzymatic reaction. We motivate our approach further by showing that it also efficiently describes the effects of interventions on functional laws such as the ideal gas law.

A tree has many analogies in real life, and turns out that it has influenced a wide area of machine learning, covering both classification and regression. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. If you're working towards an understanding of machine learning, it's important to know how to work with decision trees. This course covers the essentials of machine learning, including predictive analytics and working with decision trees. In this course, we'll explore several popular tree algorithms and learn how to use reverse engineering to identify specific variables.