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How about using Facebook's Prophet package for time series forecasting in Alteryx Designer? With Prophet, you are not stuck with the results of a completely automatic procedure if the forecast is not satisfactory — an analyst with no training in time series methods can improve or tweak forecasts using a variety of easily-interpretable parameters.
Have you struggled to deploy your predictive models in a timely manner before they become obsolete? This article will show you how Alteryx Promote solves this challenge by deploying your model into a RESTful API that can be called from a wide variety of enterprise applications.
There are two types of model errors when making an estimate; bias and variance. Understanding both of these types of errors, as well as how they relate to one another is fundamentally important to understanding model overfitting, underfitting, and complexity.
Most real-world data-science design patterns combine several models to solve a single business problem. This post surveys the most common and effective techniques for combining models. Once you make it through this post (and its predecessors), you'll be ready to take on the design patterns we'll begin learning in 2017.
Cross validation (CV) is a difficult topic. There are many ways to do CV, and articles on the subject can be very technical. This blog post is a gentle introduction to CV. Read it and you'll find it much easier to understand later posts describing data-science design patterns that use CV.