Decision Tree Learning


Want to know how to choose Machine Learning algorithm?

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Machine Learning is the foundation for today's insights on customer, products, costs and revenues which learns from the data provided to its algorithms. Some of the most common examples of machine learning are Netflix's algorithms to give movie suggestions based on movies you have watched in the past or Amazon's algorithms that recommend products based on other customers bought before. Decision Trees: Decision tree output is very easy to understand even for people from non-analytical background. It does not require any statistical knowledge to read and interpret them. Fastest way to identify most significant variables and relation between two or more variables.


Finding the Root - Jason M. Pittman

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You may have thought we were done with decisions trees. I am done with respect to discussing general approaches and types of problems. You could say that we're moving from a view of the forest, to finding the root for our tree. However, there is a bit more to explore when it comes to the underlying mathematical functions associated with navigating data to construct our trees. In our last discussion, I introduced the concept of a cost function and gave a specific example in the Gini coefficient.


A Quick look at ML in algorithmic trading strategies Packt Hub

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Algorithmic trading relies on computer programs that execute algorithms to automate some, or all, elements of a trading strategy. Algorithms are a sequence of steps or rules to achieve a goal and can take many forms. In the case of machine learning (ML), algorithms pursue the objective of learning other algorithms, namely rules, to achieve a target based on data, such as minimizing a prediction error. In this article, we have a look at use cases of ML and how it is used in algorithmic trading strategies. These algorithms encode various activities of a portfolio manager who observes market transactions and analyzes relevant data to decide on placing buy or sell orders.


Random Forest Algorithm in Machine Learning

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Random forest algorithm is a one of the most popular and most powerful supervised Machine Learning algorithm in Machine Learning that is capable of performing both regression and classification tasks. As the name suggest, this algorithm creates the forest with a number of decision trees. Random Forest Algorithm in Machine Learning: Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making.


ML: Hukou System and Health Outcomes

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University of Johannesburg and CIRANO; 4 / 46 AEA 2019 - Atlanta SKEMA Introduction Introduction China's rapid development have spurred large migration from rural areas to urban areas Between 1990 and the end of 2015 the proportion of China's population living in urban areas jumped from 26% to 56% Currently estimated by census, there are more than 240 million rural-to-urban migrants and more than 160 million working in cities outside of their hukou.


Artificial Intelligence Decision Tree

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In this article we will discuss decision points for selecting right components for Artificial Intelligence (AI) solutions. This is also an update to Machine Learning Decision Tree (v1). Keep in mind here that AI is a broader term compared to Machine Learning.


Classification and Regression Trees

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Learn about CART in this guest post by Jillur Quddus, a lead technical architect, polyglot software engineer and data scientist with over 10 years of hands-on experience in architecting and engineering distributed, scalable, high-performance, and secure solutions used to combat serious organized crime, cybercrime, and fraud. Although both linear regression models allow and logistic regression models allow us to predict a categorical outcome, both of these models assume a linear relationship between variables. Classification and Regression Trees (CART) overcome this problem by generating Decision Trees. These decision trees can then be traversed to come to a final decision, where the outcome can either be numerical (regression trees) or categorical (classification trees). When traversing decision trees, start at the top.


Silencing Malware with AI

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Stuart McClure is on a personal mission. After more than two decades in the anti-malware industry, he firmly believes that ninety percent of malware attacks today can be prevented by not clicking on this, not clicking on that, and not opening that attachment either. While he's not the first nor alone in suggesting the user bears at least some responsibility, the anti-malware industry up until now hasn't yet produced an effective alternative to signature-based solutions based on known attacks. McClure's company, Cylance, thinks it has the answer with its first-generation AI-driven anti-malware products for both enterprises and consumers. "Why couldn't we simply train a computer to think like a cybersecurity professional to know what to do and not to do based on the characteristics and features of known attacks?" asked McClure.


Improve Machine Learning Results with Ensemble Learning

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NOTE: This article assumes that you are familiar with a basic understanding of Machine Learning algorithms. Suppose you want to buy a new mobile phone, will you walk directly to the first shop and purchase the mobile based on the advice of shopkeeper? You would visit some of the online mobile seller sites where you can see a variety of mobile phones, their specifications, features, and prices. You may also consider the reviews that people posted on the site. However, you probably might also ask your friends and colleagues for their opinions.


A Comprehensive Guide to Decision Tree Learning

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Decision Tree is one of the most widely used supervised machine learning algorithm (a dataset which has been labeled) for inductive inference. Decision tree learning is a method for approximating discrete valued target functions in which the function which is learned during the training is represented by a decision tree. The learned tree can also be represented as nested if-else rule to improve human readability. Decision tree learning is used for classification as well as regression is often called as classification tree and regression tree respectively. The term Classification And Regression Tree (CART) analysis is used to refer both the tasks.