Learning to Classify with Branching Tests: "A decision tree takes as input an object or situation described by a set of properties, and outputs a yes/no decision. Decision trees therefore represent Boolean functions. Functions with a larger range of outputs can also be represented...."
– Artificial Intelligence: A Modern Approach. By Stuart Russell & Peter Norvig. 2002. Section 18.3; page 531.
Restaurants are an essential part of a country's economy and society. Whether it may be for social gatherings or a quick bite, most of us have experienced at least one visit. With the recent rise in pop up restaurants and food trucks, it's imperative for the business owner to figure out when and where to open new restaurants since it takes up a lot of time, effort, and capital to do so. This brings up the problem of finding the best optimal time and place to open a new restaurant. TFI which owns many giant restaurant chains has provided demographic, real estate, and commercial data in their restaurant revenue prediction on Kaggle.
Do you understand how your machine learning model works? Despite the ever-increasing usage of machine learning (ML) and deep learning (DL) techniques, the majority of companies say they can't explain the decisions of their ML algorithms . This is, at least in part, due to the increasing complexity of both the data and models used. It's not easy to find a nice, stable aggregation over 100 decision trees in a random forest to say which features were most important or how the model came to the conclusion it did. This problem grows even more complex in application domains such as computer vision (CV) or natural language processing (NLP), where we no longer have the same high-level, understandable features to help us understand the model's failures.
In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.
In this series, we will start by discussing how to train, visualize, and make predictions with Decision trees. After that, we will go through a training algorithm known as CART which is used by Scikit-learn, and lastly, we would discuss how to regularize the trees and use them for regression tasks. Decision trees are versatile machine learning algorithm capable of performing both regression and classification task and even work in case of tasks which has multiple outputs. They are powerful algorithms, capable of fitting even complex datasets. They are also the fundamental components of Random Forests, which is one of the most powerful machine learning algorithms available today.
Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on "Big Data," it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods. Machine learning is a branch of computer science that broadly aims to enable computers to "learn" without being directly programmed (1). It has origins in the artificial intelligence movement of the 1950s and emphasizes practical objectives and applications, particularly prediction and optimization. Computers "learn" in machine learning by improving their performance at tasks through "experience" (2, p. xv). In practice, "experience" usually means fitting to data; hence, there is not a clear boundary between machine learning and statistical approaches. Indeed, whether a given methodology is considered "machine learning" or "statistical" often reflects its history as much as genuine differences, and many algorithms (e.g., least absolute shrinkage and selection operator (LASSO), stepwise regression) may or may not be considered machine learning depending on who you ask. Still, despite methodological similarities, machine learning is philosophically and practically distinguishable. At the liberty of (considerable) oversimplification, machine learning generally emphasizes predictive accuracy over hypothesis-driven inference, usually focusing on large, high-dimensional (i.e., having many covariates) data sets (3, 4). Regardless of the precise distinction between approaches, in practice, machine learning offers epidemiologists important tools. In particular, a growing focus on "Big Data" emphasizes problems and data sets for which machine learning algorithms excel while more commonly used statistical approaches struggle. This primer provides a basic introduction to machine learning with the aim of providing readers a foundation for critically reading studies based on these methods and a jumping-off point for those interested in using machine learning techniques in epidemiologic research.
Decision Tree Algorithm one of the easiest and popular Algorithms to predict the output. The Decision Tree Algorithm is a part of the supervised machine learning algorithm. Here the problem is represented in a form of a tree to predict the outcome. This algorithm aims to create a model that should predict the value of a variable that is targeted, and for this purpose, it is represented in a form of a decision tree. It is used for classification problems and also for regression problems.
In a previous post I briefly touched on the problem with overfitting, which is loosely defined as a machine learning model that memorizes a training data set and thus provides high accuracy for predictions using it, but then performs poorly when presented with new data -- a phenomenon known as variance. The post discussed the Random Forest approach using bootstrap aggregation to address this issue, but it begged the question: "Why does intentionally producing lower-quality data sets and averaging across their results produce better predictions?" Reality, it turns out, is messy, so intentionally introducing inaccuracy in the process of producing predictions (that's some impressive alliteration, don't you think?) usually makes them better. It's a process known as regularization. It turns out that all kinds of machine learning algorithms have overfitting risks, and they way you regularize depends on the model you're trying to fit.
This article will cover one of the most advanced algorithms and most widely used in analytical applications. This is an extensive subject, as we have several algorithms and various techniques for working with decision trees. On the other hand, these algorithms are among the most powerful in Machine Learning and are easy to interpret. So, let's start by defining what decision trees are and their representation through machine learning algorithms. For decision tree learning models, we will study some algorithms with C4.5, C5.0, CART, and ID3.
AWS pushes Sagemaker as its machine learning platform. However, Spark's MLlib is a comprehensive library that runs distributed ML natively on AWS Glue -- and provides a viable alternative to their primary ML platform. One of the big benefits of Sagemaker is that it easily supports experimentation via its Jupyter Notebooks. But operationalising your Sagemaker ML can be difficult, particularly if you need to include ETL processing at the start of your pipeline. In this situation, Apache Spark's MLlib running on AWS Glue can be a good option -- by its very nature, it is immediately operationalised, integrated with ETL pre-processing and ready to be used in production for an end-to-end machine learning pipeline.