# Category Archives: Regression

## Genetic algorithms and symbolic regression

A few months ago, I wrote a post about optimization using gradient descent, which involves searching for a model that best meets certain criteria by repeatedly making adjustments that improve things a little bit at a time. In many situations, this works … Continue reading

## Optimization

Optimization is a topic that has come up in a number of posts on this blog, but that I’ve never really addressed directly. So, I though it was about time that I gave it its own post. The term “optimization” … Continue reading

Posted in Classification, Regression | 3 Comments

## Logistic regression

In the last post, I introduced the Support Vector Machine (SVM) algorithm, which attempts to find a line/plane/hyperplane that separates the two classes of points in a given data set. This algorithm adapts elements of linear regression, a statistical tool (namely, … Continue reading

Posted in Classification, Regression | 18 Comments

## General regression and over fitting

In the last post, I discussed the statistical tool called linear regression for different dimensions/numbers of variables and described how it boils down to looking for a distribution concentrated near a hyperplane of dimension one less than the total number … Continue reading

Posted in Modeling, Regression | 14 Comments

## The geometry of linear regression

In this post, we’ll warm up our geometry muscles by looking at one of the most basic data analysis techniques: linear regression. You’ve probably encountered it elsewhere, but I want to think about it from the point of view of … Continue reading

Posted in Modeling, Regression | 28 Comments