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.
Data Science is considered as one of the most modern and fascinating jobs of our time. It can be funny and can give you satisfaction, but is it really as it's described? At the beginning of their career, Data Scientists think that Data Science is a wonderful, magical world full of algorithms, Python functions that performs every possible spell with a line of code and statistical models able to detect the most useful correlations among data that could make you an invincible superhero in your company. You start dreaming about your CEO congratulating with you and shaking your hand, you begin to see decision trees and clusters everywhere and, of course, the most terrifying neural network architectures your mind can dream. But since the very first day of your first Data Science project, you start to realize what reality is.
The sustained success random forests has led naturally to the desire to better understand the statistical and mathematical properties of the procedure. Lin and Jeon (2006) introduced the potential nearest neighbor framework and Biau and Devroye (2010) later established related consistency properties. In the last several years, a number of important statistical properties of random forests have also been established whenever base learners are constructed with subsamples rather than bootstrap samples. Scornet et al. (2015) provided the first consistency result for Breiman's original random forest algorithm whenever the true underlying regression function is assumed to be additive. Despite the impressive volume of research from the past two decades and the exciting recent progress in establishing their statistical properties, a satisfying explanation for the sustained empirical success of random forests has yet to be provided.
Let's start by understanding what decision trees are because they are the fundamental units of a random forest classifier. At a high level, decision trees can be viewed as a machine learning construct used to perform either classification or regression on some data in a hierarchical structure. In this article, I will only discuss the use of decision trees for classification. Decision trees use machine learning to identify key differentiating factors between the different classes of our data. By doing so, decision trees can take some input data and predict a class by running the data through a set of differentiating questions that it forms using machine learning.
In this SAS How To Tutorial, Cat Truxillo shows you how to train forest models in SAS. There are multiple ways to train forest models. Cat will show you how to train a forest using two different point-and-click methods. The first method uses SAS Visual Analytics while in the second example, Cat trains a forest in Model Studio, using SAS Viya. Before diving into the examples of how to create a forest model, Cat explains random forest and answers the question "what are random forests?".
A decision tree is a useful machine learning algorithm used for both regression and classification tasks. The name "decision tree" comes from the fact that the algorithm keeps dividing the dataset down into smaller and smaller portions until the data has been divided into single instances, which are then classified. If you were to visualize the results of the algorithm, the way the categories are divided would resemble a tree and many leaves. That's a quick definition of a decision tree, but let's take a deep dive into how decision trees work. Having a better understanding of how decision trees operate, as well as their use cases, will assist you in knowing when to utilize them during your machine learning projects.
On a meetup that I attended a couple of months ago in Sydney, I was introduced to an online machine learning course by fast.ai. I never paid any attention to it then. This week, while working on a Kaggle competition, and looking for ways to improve my score, I came across this course again. I decided to give it a try. Here is what I learned from the first lecture, which is a 1 hour 17 minutes video on INTRODUCTION TO RANDOM FOREST.
Anyone who has built a machine learning model will know the feeling… "How do I get my masterpiece out of this python notebook and in front of the world?". Answering this question is rarely simple and with a multitude of different options to consider, this can be a huge source of both technical debt for data science teams and dependency on engineering resource. At HeadBox we have developed a lean deployment pipeline for simple machine learning models that are used in our venue recommendation engines. Here I will demonstrate the deployment of a simple classification model using three Serverless lambda functions, pulling data from a data warehouse such as Snowflake, posting results to S3 buckets and DynamoDB tables, as well as posting daily performance updates to slack. Our first Serverless function will be used to pull training data from Snowflake, perform feature engineering and train a simple decision tree model.
The course is created on the basis of three pillars of learning: Know (Study) Do (Practice) Review (Self feedback) Know We have created a set of concise and comprehensive videos to teach you all the Excel related skills you will need in your professional career. Do With each lecture, we have provide a practice sheet to complement the learning in the lecture video. These sheets are carefully designed to further clarify the concepts and help you with implementing the concepts on practical problems faced on-the-job. Review Check if you have learnt the concepts by comparing your solutions provided by us. Ask questions in the discussion board if you face any difficulty.