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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
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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
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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
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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
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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
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