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.

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Say, you appeared for the position of Statistical analyst at WalmartLabs. Now like most of the companies, you don't just have one round of interview. You have multiple rounds of interviews. Each one of these interviews is chaired by independent panels. Generally, even the questions asked in these interviews differ from each other.

The value is not in software, the value is in data, and this is really important for every single company, that they understand what data they've got. More and more companies are now aware of the power of data. Machine Learning models are increasing in popularity and are now being used to solve a wide variety of business problems using data. Having said that, it is also true that there is always a trade-off between accuracy of models & its interpretability. In general, if accuracy has to be improved, data scientists have to resort to using complicated algorithms like Bagging, Boosting, Random Forests etc. which are "Blackbox" methods.

You are going to learn the most popular classification algorithm. Which is the Random forest algorithm. As a motivation to go further I am going to give you one of the best advantages of random forest. The Same algorithm both for classification and regression, You mind be thinking I am kidding. But the truth is, Yes we can use the same random forest algorithm both for classification and regression.

Tree ensemble models such as random forests and boosted trees are among the most widely used and practically successful predictive models in applied machine learning and business analytics. Although such models have been used to make predictions based on exogenous, uncontrollable independent variables, they are increasingly being used to make predictions where the independent variables are controllable and are also decision variables. In this paper, we study the problem of tree ensemble optimization: given a tree ensemble that predicts some dependent variable using controllable independent variables, how should we set these variables so as to maximize the predicted value? We formulate the problem as a mixed-integer optimization problem. We theoretically examine the strength of our formulation, provide a hierarchy of approximate formulations with bounds on approximation quality and exploit the structure of the problem to develop two large-scale solution methods, one based on Benders decomposition and one based on iteratively generating tree split constraints. We test our methodology on real data sets, including two case studies in drug design and customized pricing, and show that our methodology can efficiently solve large-scale instances to near or full optimality, and outperforms solutions obtained by heuristic approaches. In our drug design case, we show how our approach can identify compounds that efficiently trade-off predicted performance and novelty with respect to existing, known compounds. In our customized pricing case, we show how our approach can efficiently determine optimal store-level prices under a random forest model that delivers excellent predictive accuracy.

Simple Decision Tree is an Excel Add-in created by Thomas Seyller. The Add-in is released under the terms of GPL v3 with additional permissions. Thomas created this Add-in for the Stanford Decisions and Ethics Center and open-sourced it for the Decision Professionals Network. This software has been extensively used to teach Decision Analysis at Stanford University.

This study seeks to validate a search protocol of ill health-related terms using Twitter data which can later be used to understand if, and how, Twitter can reveal information on the current health situation. We extracted conversations related to health and disease postings on Twitter using a set of pre-defined keywords, assessed the prevalence, frequency, and timing of such content in these conversations, and validated how this search protocol was able to detect relevant disease tweets. Classification and Regression Trees (CART) algorithm was used to train and test search protocols of disease and health hits comparing to those identified by our team. The accuracy of predictions showed a good validity with AUC beyond 0.8. Our study shows that monitoring of public sentiment on Twitter can be used as a real-time proxy for health events.

Scikit-Learn is a popular library for Machine Learning in python programming language. If you want to test your knowledge with just a few lines of code, scikit-learn is what you need. From Linear and Logistic Regression to SVM and KNN, you name and scikit-learn has it. You will often need to prepare and transform your data in a form that is suitable for scikit-learn to use for training the models. Pandas is an awesome library for python which can be used for this purpose.

When discussing classifiers, decision trees are often thought of as easily interpretable models when compared to numerous more complex classifiers, especially those of the blackbox variety. And this is generally true. This is especially true of rather comparatively simple models created from simple data. This is much-less true of complex decision trees crafted from large amounts of (high-dimensional) data. Even otherwise straightforward decision trees which are of great depth and/or breadth, consisting of heavy branching, can be difficult to trace.