In this post, I will provide an overview of generalized additive models (GAMs) and their desirable features. Predictive accuracy has long been an important goal of machine learning. But model interpretability has received more attention in recent years. Stakeholders, such as executives, regulators, and domain experts, often want to understand how and why a model makes its predictions before they trust it enough to use it in practice. However, when you train a machine learning model, you typically face a tradeoff between accuracy and interpretability.

Ever since automated machine learning has entered the scene, people are asking, "Will automated machine learning replace data scientists?" I personally don't think we need to be worried about losing our jobs any time soon. Automated machine learning is great at efficiently trying a lot of different options and can save a data scientist hours of work. The caveat is that automated machine learning cannot replace all tasks. Automated machine learning does not understand context as well as a human being.

I would actually use SAS Visual Data Mining and Machine Learning on Viya (VDMML) as the frame of reference to Enterprise Miner (EM). Viya is the framework on which VDMML runs. As the Product Management lead for SAS Machine Learning products, I position VDMML as an evolution of EM. For years, EM has been the go-to data mining and machine learning tool for running end-to-end model tournaments, from feature engineering, selection, and extraction, modeling, and most importantly - deployment. As a client-server product, it enabled both desktop and server users to access this environment.

In this SAS How To Tutorial, Jeff Thompson shows how to compare models in SAS. When working to solve business problems, analysts and data scientist often build many models. These models are likely based on different algorithms such as trees and tree-based models, neural networks, and even traditional regression models. But how do they know which model is best? That is where model comparison comes in and Jeff will step through how to perform that in SAS using Model Studio. He then runs through the different types of predictions you may be trying to make and which numeric statistics are appropriate for each prediction type.

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?".

I started my MSc Business Analytics course at theh University of Surrey almost one year ago. I had no prior experience in Machine Learning or data science. Before, I used to develop and manage EU projects for businesses, local authorities and non-profit organisations. I even achieved two International awards for best project. However, I wanted to immerse in the technology field and be part of the great community which enhance business by developing the products and services of the future.

Managing models can sometimes be tricky. SAS Viya 3.3 can help you with this! This post provides a few highlights regarding exporting, importing, and managing models in SAS Viya 3.3 using SAS VA VS VDMML 8.2 and SAS Model Manager 15.1. Specifically, I will touch on the Explore and Visualize Data, Build Models (Model Studio) and Manage Models (Model Manager) interfaces. Models built in this interface can be exported for a variety of reasons, for example to run in Hadoop.