We all use Decision Tree technique on daily basis to plan our life, we just don't give a fancy name to those decision-making process. Businesses use these supervised machine learning techniques like Decision trees to make better decisions and make more profit. Decision trees have been around for a long time and also known to suffer from bias and variance. You will have a large bias with simple trees and a large variance with complex trees. Ensemble methods, which combines several decision trees to produce better predictive performance than utilizing a single decision tree.

A decision tree is a tree-shaped diagram that shows statistical probability or determines a course of action. It shows the steps to take and why one choice may lead to another. Therefore, it is a suitable decision-making tool for research analysis or for planning the strategy to reach a goal. A decision tree has three main parts: a root node, leaf nodes, and branches. The root node is the target value that we are seeking to reach.

Conventional seismic techniques for detecting the subsurface geologic features are challenged by limited data coverage, computational inefficiency, and subjective human factors. We developed a novel data-driven geological feature detection approach based on pre-stack seismic measurements. Our detection method employs an efficient and accurate machine-learning detection approach to extract useful subsurface geologic features automatically. Specifically, our method is based on kernel ridge regression model. The conventional kernel ridge regression can be computationally prohibited because of the large volume of seismic measurements. We employ a data reduction technique in combination with the conventional kernel ridge regression method to improve the computational efficiency and reduce memory usage. In particular, we utilize a randomized numerical linear algebra technique, named Nystr\"om method, to effectively reduce the dimensionality of the feature space without compromising the information content required for accurate detection. We provide thorough computational cost analysis to show efficiency of our new geological feature detection methods. We further validate the performance of our new subsurface geologic feature detection method using synthetic surface seismic data for 2D acoustic and elastic velocity models. Our numerical examples demonstrate that our new detection method significantly improves the computational efficiency while maintaining comparable accuracy. Interestingly, we show that our method yields a speed-up ratio on the order of $\sim10^2$ to $\sim 10^3$ in a multi-core computational environment.

As the data that is fed becomes larger, the decision tree tends to become longer. In such cases, noise and corrupt/incorrect data can have a detrimental impact on the decision tree. This results in the decision tree overfitting the dataset, that is decision tree performs satisfactory for the training data, but fails to produce an appropriate approximation of the target concept when it encounters actual data. Overfitting can also occur when insufficent data is provided to build the decision tree (like perhaps, our previous with only 6 rows.)

Decision Tree algorithm belongs to the family of supervised learning algorithms. Unlike other supervised learning algorithms, decision tree algorithm can be used for solving regression and classification problems too. The general motive of using Decision Tree is to create a training model which can use to predict class or value of target variables by learning decision rules inferred from prior data(training data). The understanding level of Decision Trees algorithm is so easy compared with other classification algorithms. The decision tree algorithm tries to solve the problem, by using tree representation.

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.