# Tag: Linear Regression (15)

**Using Linear Regression for Predictive Modeling in R**- Jun 1, 2018.

In this post, we’ll use linear regression to build a model that predicts cherry tree volume from metrics that are much easier for folks who study trees to measure.**A Tour of The Top 10 Algorithms for Machine Learning Newbies**- Feb 26, 2018.

For machine learning newbies who are eager to understand the basic of machine learning, here is a quick tour on the top 10 machine learning algorithms used by data scientists.**Using TensorFlow for Predictive Analytics with Linear Regression**- Nov 21, 2017.

This post presents a powerful and simple example of how to use TensorFlow to perform a Linear Regression. check out the code for your own experiments!**You have created your first Linear Regression Model. Have you validated the assumptions?**- Nov 15, 2017.

Linear Regression is an excellent starting point for Machine Learning, but it is a common mistake to focus just on the p-values and R-Squared values while determining validity of model. Here we examine the underlying assumptions of a Linear Regression, which need to be validated before applying the model.**Top 10 Machine Learning Algorithms for Beginners**- Oct 20, 2017.

A beginner's introduction to the Top 10 Machine Learning (ML) algorithms, complete with figures and examples for easy understanding.

**Learn Generalized Linear Models (GLM) using R**- Oct 11, 2017.

In this article, we aim to discuss various GLMs that are widely used in the industry. We focus on: a) log-linear regression b) interpreting log-transformations and c) binary logistic regression.**Is Regression Analysis Really Machine Learning?**- Jun 5, 2017.

What separates "traditional" applied statistics from machine learning? Is statistics the foundation on top of which machine learning is built? Is machine learning a superset of "traditional" statistics? Do these 2 concepts have a third unifying concept in common? So, in that vein... is regression analysis actually a form of machine learning?**Getting Up Close and Personal with Algorithms**- Mar 21, 2017.

We've put together a brief summary of the top algorithms used in predictive analysis, which you can see just below. Read to learn more about Linear Regression, Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, and more.**Regression Analysis: A Primer**- Feb 6, 2017.

Despite the popularity of Regression, it is also misunderstood. Why? The answer might surprise you: There is no such thing as Regression. Rather, there are a large number of statistical methods that are called Regression, all of which are based on a shared statistical foundation.**Linear Regression, Least Squares & Matrix Multiplication: A Concise Technical Overview**- Nov 24, 2016.

Linear regression is a simple algebraic tool which attempts to find the “best” line fitting 2 or more attributes. Read here to discover the relationship between linear regression, the least squares method, and matrix multiplication.**Webinar: Modern Regression Modeling for Voter MicroTargeting, Sep 14, Sep 21**- Sep 7, 2016.

Join us for a special 2-part webinar about voting trends, and we will show how machine learning models and data science can be used in elections.**What is the Role of the Activation Function in a Neural Network?**- Aug 30, 2016.

Confused as to exactly what the activation function in a neural network does? Read this overview, and check out the handy cheat sheet at the end.**A Neat Trick to Increase Robustness of Regression Models**- Aug 22, 2016.

Read this take on the validity of choosing a different approach to regression modeling. Why isn't L1 norm used more often?**The Gentlest Introduction to Tensorflow – Part 1**- Aug 17, 2016.

In this series of articles, we present the gentlest introduction to Tensorflow that starts off by showing how to do linear regression for a single feature problem, and expand from there.**And the Winner is… Stepwise Regression**- Aug 1, 2016.

This post evaluates several methods for automating the feature selection process in large-scale linear regression models and show that for marketing applications the winner is Stepwise regression.