Machine Learning and Applied Statistics Lesson of the Day – How to Construct Receiver Operating Characteristic Curves

A receiver operating characteristic (ROC) curve is a 2-dimensional plot of the \text{Sensitivity} (the true positive rate) versus 1 - \text{Specificity} (1 minus the true negative rate) of a binary classifier while varying its discrimination threshold.  In statistics and machine learning, a basic and popular tool for binary classification is logistic regression, and an ROC curve is a useful way to assess the predictive accuracy of the logistic regression model.

To illustrate with an example, let’s consider the Bernoulli response variable Y and the covariates X_1, X_2, ..., X_p.  A logistic regression model takes the covariates as inputs and returns P(Y = 1).  You as the user of the model must decide above which value of P(Y = 1) you will predict that Y = 1; this value is the discrimination threshold.  A common threshold is P(Y = 1) = 0.5.

Once you finish fitting the model with a training set, you can construct an ROC curve by following these steps below:

  1. Set a discrimination threshold.
  2. Use the covariates to predict Y for each observation in a validation set.
  3. Since you have the actual response values in the validation set, you can then calculate the sensitivity and specificity for your logistic regression model at that threshold.
  4. Repeat Steps 1-3 with a new threshold.
  5. Plot the values of \text{Sensitivity} versus 1 - \text{Specificity} for all thresholds.  The result is your ROC curve.

The use of a validation set to assess the predictive accuracy of a model is called validation, and it is a good practice for supervised learning.  If you have another fresh data set, it is also good practice to use that as a test set to assess the predictive accuracy of your model.

Note that you can perform Steps 2-5 for the training set, too – this is often done in statistics when you don’t have many data to work with, and the best that you can do is to assess the predictive accuracy of your model on the data set that you used to fit the model.

Presentation Slides: Machine Learning, Predictive Modelling, and Pattern Recognition in Business Analytics

I recently delivered a presentation entitled “Using Advanced Predictive Modelling and Pattern Recognition in Business Analytics” at the Statistical Society of Canada’s (SSC’s) Southern Ontario Regional Association (SORA) Business Analytics Seminar Series.  In this presentation, I

– discussed how traditional statistical techniques often fail in analyzing large data sets

– defined and described machine learning, supervised learning, unsupervised learning, and the many classes of techniques within these fields, as well as common examples in business analytics to illustrate these concepts

– introduced partial least squares regression and bootstrap forest (or random forest) as two examples of supervised learning (0r predictive modelling) techniques that can effectively overcome the common failures of traditional statistical techniques and can be easily implemented in JMP

– illustrated how partial least squares regression and bootstrap forest were successfully used to solve some major problems for 2 different clients at Predictum, where I currently work as a statistician

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Presentation Slides – Overcoming Multicollinearity and Overfitting with Partial Least Squares Regression in JMP and SAS

My slides on partial least squares regression at the Toronto Area SAS Society (TASS) meeting on September 14, 2012, can be found here.

My Presentation on Partial Least Squares Regression

My first presentation to Toronto Area SAS Society (TASS) was delivered on September 14, 2012.  I introduced a supervised learning/predictive modelling technique called partial least squares (PLS) regression; I showed how normal linear least squares regression is often problematic when used with big data because of multicollinearity and overfitting, explained how partial least squares regression overcomes these limitations, and illustrated how to implement it in SAS and JMP.  I also highlighted the variable importance for projection (VIP) score that can be used to conduct variable selection with PLS regression; in particular, I documented its effectiveness as a technique for variable selection by comparing some key journal articles on this issue in academic literature.


The green line is an overfitted classifier.  Not only does it model the underlying trend, but it also models the noise (the random variation) at the boundary.  It separates the blue and the red dots perfectly for this data set, but it will classify very poorly on a new data set from the same population.

Source: Chabacano via Wikimedia
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