Decision trees are a type of recursive partitioning algorithm. Decision trees are built up of two types of nodes: decision nodes, and leaves. The decision tree starts with a node called the root. If the root is a leaf then the decision tree is trivial or degenerate and the same classification is made for all data. For decision nodes we examine a single variable and move to another node based on the outcome of a comparison.

"How long would you say that's been going on?" In primary care, one in 20 patients will get a wrong diagnosis. These are worrying figures, driven by the complex nature of diagnosis, which can encompass incomplete information from patients, missed hand-offs between care providers, biases that cloud doctors' judgement, overworked staff, overbooked systems, and more. This is why many want to use the constant and unflappable power of artificial intelligence to achieve more accurate diagnosis, prompt care and greater efficiency.

The last decade has seen a surge of interest in adaptive learning algorithms for data stream classification, with applications ranging from predicting ozone level peaks, learning stock market indicators, to detecting computer security violations. In addition, a number of methods have been developed to detect concept drifts in these streams. Consider a scenario where we have a number of classifiers with diverse learning styles and different drift detectors. Intuitively, the current 'best' (classifier, detector) pair is application dependent and may change as a result of the stream evolution. Our research builds on this observation. We introduce the $\mbox{Tornado}$ framework that implements a reservoir of diverse classifiers, together with a variety of drift detection algorithms. In our framework, all (classifier, detector) pairs proceed, in parallel, to construct models against the evolving data streams. At any point in time, we select the pair which currently yields the best performance. We further incorporate two novel stacking-based drift detection methods, namely the $\mbox{FHDDMS}$ and $\mbox{FHDDMS}_{add}$ approaches. The experimental evaluation confirms that the current 'best' (classifier, detector) pair is not only heavily dependent on the characteristics of the stream, but also that this selection evolves as the stream flows. Further, our $\mbox{FHDDMS}$ variants detect concept drifts accurately in a timely fashion while outperforming the state-of-the-art.

When something is wrong we go to the doctor and we begin what is ideally a two-step process: diagnosis and therapy. Artificial intelligence (AI) will transform both aspects of health care by adding powerful new tools to the doctor's bag. A range of diverse, compelling research projects around AI-driven diagnoses are underway. For example, an international community (including Google's Brain project) competes in an annual challenge to correctly diagnose breast cancer in 400 expert-labeled microscopic images of biopsy samples. About 99 percent of the slides were identified correctly by a project from Harvard and Massachusetts Institute of Technology.

Machine learning is a buzzword in the technology world right now. It is fun, challenging, puzzling, and even a bit scary if you're one of those people who believes robots will someday steal our jobs and rule the world. Whether we like it or not, we are surrounded by adaptive smart things that can fix some of our most common daily queries in a split second. Machine learning was embodied in the famous Skynet from the Terminator franchise. Some are afraid of this fantasy becoming real, while some are excited about a whole new world of opportunities we'll get in the AI world.

Decision trees are used extensively in machine learning because they are easy to use, easy to interpret, and easy to operationalize. KD Nuggets, one of the most respected sites for data science and machine learning, recently published an article that identified decision trees as a "top 10" algorithm for machine learning. If you are new to machine learning, some of these concepts may be unfamiliar. The goal of this blog is to provide you with the basics of decision trees using Talend and Apache Spark. If you want to learn more about advanced analytics, please see the references section below.(2)

As a part of a series of posts discussing how a machine learning classifier works, I ran decision tree to classify a XY-plane, trained with XOR patterns or linearly separable patterns. Its decision boundary was drawn almost perfectly parallel to the assumed true boundary, i.e. Awful result, it appears to never follow the true boundary. Just a little improved, but it still appears to be overfitted. Even worse... it appears to get more overfitted than the case of 2-classes.

I use the methodology you speak of all the time. I was the original programer for Breiman and Stone's version of CART in the late 70's which is where I believe I was first introduced to that method. However we were very careful to use the term variation explained since there is little relationship to the theoretical Pearson "r". Be aware that this value can go negative. Which implies that parts of your model behave a lot higher variation then the population variance.

This article is by Jitesh Shah, a data & stats jockey in perpetual beta, located in Fremont, California. This article includes the data set and Python code. Wouldn't it be nice if defects and product failures can be predicted in advance. We've got the data on attributes and design features and manufacturing processes that come together and creates that product and we have defect and failure rate data so all we got to do is connect the two and use that to predict which set of features and attributes and processes in combination cause these defects. That was probably a non-trivial endeavor in the past but now with the ability to store and process vast amounts of data (no secret there), no big deal.