Model-combining (i.e., mixing) methods have been proposed in recent years to deal with uncertainty in model selection. Even though advantages of model combining over model selection have been ...

We introduce a fast stepwise regression method, called the orthogonal greedy algorithm (OGA), that selects input variables to enter a p-dimensional linear regression model (with p ≫ n, the sample size ...

Businesspeople need to demand more from machine learning so they can connect data scientists’ work to relevant action. This requires basic machine learning literacy — what kinds of problems can ...

The purpose of this tutorial is to continue our exploration of regression by constructing linear models with two or more explanatory variables. This is an extension of Lesson 9. I will start with a ...

Overview Regression explains how changes in one factor influence another with clarity.Each regression type is suited for ...

If you want to advance your data science skill set, Python can be a valuable tool for SEOs to generate deep data insights to help your brand. The programming language of Python is gaining popularity ...

The first step in running regression analysis in Excel is to double-check that the free plugin Data Analysis ToolPak is installed. This plugin makes calculating a range of statistics very easy. It is ...

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single ...