• Decision Trees

    BACK: Improving Linear Models UNDER HEAVY CONSTRUCTION: NOT EVERYTHING MAY BE CORRECT. trees NEXT: Support Vector Machines

  • Improving Linear Models

    BACK: Resampling Methods UNDER HEAVY CONSTRUCTION: NOT EVERYTHING MAY BE CORRECT. But What’s wrong with least-squares regression? One of the issues with least squares is when n»p. You end up having a lot of predictive variables that don’t actually contribute much to the response, so it makes it difficult to...

  • Resampling Methods

    BACK: Classification UNDER HEAVY CONSTRUCTION: NOT EVERYTHING MAY BE CORRECT. Let’s say we created some model that fits very well to our data. Cool. But who’s to say that our model can accurately predict across different, but similar sets of data? How do we estimate the variability of our model?...

  • Classification

    BACK: Linear Regression UNDER HEAVY CONSTRUCTION: NOT EVERYTHING MAY BE CORRECT. Let’s say instead of some quantitative value (linear regression), we wanted to predict a qualitative value. Based on somebody’s \(X_1, X_2, X_3\), what is the probability that they have disease \(A, B,\) or \(C\)? Example the textbook gives: Based...

  • Statistical Learning

    BACK: Statistical Learning UNDER HEAVY CONSTRUCTION: NOT EVERYTHING MAY BE CORRECT. Recall: We assumed that the our model is linear, thus our model takes the form: \[Y=\beta_0+\beta_1*X\] I want to find some line that will model out what future values of \(Y\) would be given any input \(X\). i.e.: If...

  • Statistical Learning

    BACK: Table of Contents UNDER HEAVY CONSTRUCTION: NOT EVERYTHING MAY BE CORRECT. A good way to think about this class: \(Y = f(X) + \varepsilon\) Here, we see \(Y\) as something that we want to find out: the sales of a product, the classification of an image, etc, which is...

  • Applied Statistics Table of Contents

    BACK: Classes UNDER HEAVY CONSTRUCTION: NOT EVERYTHING MAY BE CORRECT. Preface I am currently taking this class (Fall 2018) and kind of using this page as my notes. I will be taking things out of our textbook, which can be found here. It includes datasets that will be played around...