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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
Posted in Modeling, Regression
1 Comment
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