## Analytical Chemistry Lesson of the Day – Specificity in Method Validation and Quality Assurance

In pharmaceutical chemistry, one of the requirements for method validation is specificity, the ability of an analytical method to distinguish the analyte from other chemicals in the sample.  The specificity of the method may be assessed by deliberately adding impurities into a sample containing the analyte and testing how well the method can identify the analyte.

Statistics is an important tool in analytical chemistry, and, ideally, there is no overlap in the vocabulary that is used between the 2 fields.  Unfortunately, the above definition of specificity is different from that in statistics.  In a previous Machine Learning and Applied Statistics Lesson of the Day, I introduced the concepts of sensitivity and specificity in binary classification.  In the context of assessing the predictive accuracy of a binary classifier, its specificity is the proportion of truly negative cases among the classified negative cases.

## Data Science Seminar by David Campbell on Approximate Bayesian Computation and the Earthworm Invasion in Canada

My colleague, David Campbell, will be the feature speaker at the next Vancouver Data Science Meetup on Thursday, June 25.  (This is a jointly organized event with the Vancouver Machine Learning Meetup and the Vancouver R Users Meetup.)  He will present his research on approximate Bayesian computation and Markov Chain Monte Carlo, and he will highlight how he has used these tools to study the invasion of European earthworms in Canada, especially their drastic effects on the boreal forests in Alberta.

Dave is a statistics professor at Simon Fraser University, and I have found him to be very smart and articulate in my communication with him.  This seminar promises to be both entertaining and educational.  If you will attend it, then I look forward to seeing you there!  Check out Dave on Twitter and LInkedIn.

Title: The great Canadian worm invasion (from an approximate Bayesian computation perspective)

Speaker: David Campbell

Date: Thursday, June 25

Place:

5 East 8th Avenue

Vancouver, BC

Schedule:

• 6:00 pm: Doors are open – feel free to mingle!
• 6:30 pm: Presentation begins.
• ~7:45 Off to a nearby restaurant for food, drinks, and breakout discussions.

Abstract:

After being brought in by pioneers for agricultural reasons, European earthworms have been taking North America by storm and are starting to change the Alberta Boreal forests. This talk uses an invasive species model to introduce the basic ideas behind estimating the rate of new worm introductions and how quickly they spread with the goal of predicting the future extent of the great Canadian worm invasion. To take on the earthworm invaders, we turn to Approximate Bayesian Computation methods. Bayesian statistics are used to gather and update knowledge as new information becomes available owing to their success in prediction and estimating ongoing and evolving processes. Approximate Bayesian Computation is a step in the right direction when it’s just not possible to actually do the right thing- in this case using the exact invasive species model is infeasible. These tools will be used within a Markov Chain Monte Carlo framework.

Dave Campbell is an Associate Professor in the Department of Statistics and Actuarial Science at Simon Fraser University and Director of the Management and Systems Science Program. Dave’s main research area is at the intersections of statistics with computer science, applied math, and numerical analysis. Dave has published papers on Bayesian algorithms, adaptive time-frequency estimation, and dealing with lack of identifiability. His students have gone on to faculty positions and worked in industry at video game companies and predicting behaviour in malls, chat rooms, and online sales.

## Eric’s Enlightenment for Friday, June 5, 2015

1. Christian Robert provides a gentle introduction to the Metropolis-Hastings algorithm with accompanying R codes.  (Hat Tip: David Campbell)
2. John Sall demonstrates how to perform discriminant analysis in JMP, especially for data sets with many variables.
3. Using machine learning instead of human judgment may improve the selection of job candidates.  This article also includes an excerpt from a New York Times article about how the Milwaukee Bucks used facial recognition as one justification to choose Jabari Parker over Dante Exum.  (Hat Tip: Tyler Cowen)
4. “A hospital at the University of California San Francisco Medical Center has a robot filling prescriptions.”

## Eric’s Enlightenment for Thursday, June 4, 2015

1. IBM explains how Watson the computer answered the Final Jeopardy question against Ken Jennings and Brad Rutter.  (In a question about American airports, Watson’s answer was “What is Toronto???”  It’s not as ridiculous as you think, and Watson didn’t wager a lot of money for this answer – so it still won by a wide margin.)
2. Two views on how to reform FIFA by Nate Silver and  – this is an interesting opportunity to apply good principles of institutional design and political economy.
3. How blind people navigate the Internet.
4. The Replication Network – a web site devoted to the study of replications in economics.
5. Cryptochromes and particularly the molecule flavin adenine dinucleotide (FAD) that forms part of the cryptochrome, are thought to be responsible for magnetoreception, the ability of some animals to navigate in Earth’s magnetic field.  Joshua Beardmore et al. have developed a microscope that can detect the magnetic properties of FAD – some very cool work on radical pair chemistry!

## Eric’s Enlightenment for Friday, May 1, 2015

1. PROC GLIMMIX Contrasted with Other SAS Statistical Procedures for Regression (including GENMOD, MIXED, NLMIXED, LOGISTIC and CATMOD).
2. Lee-Ping Wang et al. recently developed the nanoreactor, “a computer model that can not only determine all the possible products of the Urey-Miller experiment, but also detail all the possible chemical reactions that lead to their formation”.  What an exciting development!  It “incorporates physics and machine learning to discover all the possible ways that your chemicals might react, and that might include reactions or mechanisms we’ve never seen before”.  Here is the original paper.
3. A Quora thread on the best examples of the Law of Unintended Consequences
4. In a 2-minute video, Alex Tabarrok argues why software patents should be eliminated.

## Eric’s Enlightenment for Tuesday, April 21, 2015

1. The standard Gibbs free energy of the conversion of water from a liquid to a gas is positive.  Why does it still evaporate at room temperature?  Very good answer on Chemistry Stack Exchange.
2. The Difference Between Clustered, Longitudinal, and Repeated Measures Data.  Good blog post by Karen Grace-Martin.
3. 25 easy and inexpensive ways to clean household appliances using simple (and non-toxic) household products.
4. A nice person named Alex kindly transcribed the notes for all of Andrew Ng’s video lectures in his course on machine learning at Coursera.

## Machine Learning and Applied Statistics Lesson of the Day – Positive Predictive Value and Negative Predictive Value

For a binary classifier,

• its positive predictive value (PPV) is the proportion of positively classified cases that were truly positive.

$\text{PPV} = \text{(Number of True Positives)} \ \div \ \text{(Number of True Positives} \ + \ \text{Number of False Positives)}$

• its negative predictive value (NPV) is the proportion of negatively classified cases that were truly negative.

$\text{NPV} = \text{(Number of True Negatives)} \ \div \ \text{(Number of True Negatives} \ + \ \text{Number of False Negatives)}$

In a later Statistics and Machine Learning Lesson of the Day, I will discuss the differences between PPV/NPV and sensitivity/specificity in assessing the predictive accuracy of a binary classifier.

(Recall that sensitivity and specificity can also be used to evaluate the performance of a binary classifier.  Based on those 2 statistics, we can construct receiver operating characteristic (ROC) curves to assess the predictive accuracy of the classifier, and a minimum standard for a good ROC curve is being better than the line of no discrimination.)

## Vancouver Machine Learning and Data Science Meetup – NLP to Find User Archetypes for Search & Matching

I will attend the following seminar by Thomas Levi in the next R/Machine Learning/Data Science Meetup in Vancouver on Wednesday, June 25.  If you will also attend this event, please come up and say “Hello”!  I would be glad to meet you!

To register, sign up for an account on Meetup, and RSVP in the R Users Group, the Machine Learning group or the Data Science group.

Title: NLP to Find User Archetypes for Search & Matching

Speaker: Thomas Levi, Plenty of Fish

Location: HootSuite, 5 East 8th Avenue, Vancouver, BC

Time and Date: 6-8 pm, Wednesday, June 25, 2014

Abstract

As the world’s largest free dating site, Plenty Of Fish would like to be able to match with and allow users to search for people with similar interests. However, we allow our users to enter their interests as free text on their profiles. This presents a difficult problem in clustering, search and machine learning if we want to move beyond simple ‘exact match’ solutions to a deeper archetypal user profiling and thematic search system. Some of the common issues that arise are misspellings, synonyms (e.g. biking, cycling and bicycling) and similar interests (e.g. snowboarding and skiing) on a several million user scale. In this talk I will demonstrate how we built a system utilizing topic modelling with Latent Dirichlet Allocation (LDA) on a several hundred thousand word vocabulary over ten million+ North American users and explore its applications at POF.

Bio

Thomas Levi started out with a doctorate in Theoretical Physics and String Theory from the University of Pennsylvania in 2006. His post-doctoral studies in cosmology and string theory, where he wrote 19 papers garnering 650+ citations, then took him to NYU and finally UBC.  In 2012, he decided to move into industry, and took on the role of Senior Data Scientist at POF. Thomas has been involved in diverse projects such as behaviour analysis, social network analysis, scam detection, Bot detection, matching algorithms, topic modelling and semantic analysis.

Schedule
• 6:00PM Doors are open, feel free to mingle
• 6:30 Presentations start
• 8:00 Off to a nearby watering hole (Mr. Brownstone?) for a pint, food, and/or breakout discussions

## Machine Learning and Applied Statistics Lesson of the Day – The Line of No Discrimination in ROC Curves

After training a binary classifier, calculating its various values of sensitivity and specificity, and constructing its receiver operating characteristic (ROC) curve, we can use the ROC curve to assess the predictive accuracy of the classifier.

A minimum standard for a good ROC curve is being better than the line of no discrimination.  On a plot of

$\text{Sensitivity}$

on the vertical axis and

$1 - \text{Specificity}$

on the horizontal axis, the line of no discrimination is the line that passes through the points

$(\text{Sensitivity} = 0, 1 - \text{Specificity} = 0)$

and

$(\text{Sensitivity} = 1, 1 - \text{Specificity} = 1)$.

In other words, the line of discrimination is the diagonal line that runs from the bottom left to the top right.  This line shows the performance of a binary classifier that predicts the class of the target variable purely by the outcome of a Bernoulli random variable with 0.5 as its probability of attaining the “Success” category.  Such a classifier does not use any of the predictors to make the prediction; instead, its predictions are based entirely on random guessing, with the probabilities of predicting the “Success” class and the “Failure” class being equal.

If we did not have any predictors, then we can rely on only random guessing, and a random variable with the distribution $\text{Bernoulli}(0.5)$ is the best that we can use for such guessing.  If we do have predictors, then we aim to develop a model (i.e. the binary classifier) that uses the information from the predictors to make predictions that are better than random guessing.  Thus, a minimum standard of a binary classifier is having an ROC curve that is higher than the line of no discrimination.  (By “higher“, I mean that, for a given value of $1 - \text{Specificity}$, the $\text{Sensitivity}$ of the binary classifier is higher than the $\text{Sensitivity}$ of the line of no discrimination.)

## Machine Learning and Applied Statistics Lesson of the Day – Sensitivity and Specificity

To evaluate the predictive accuracy of a binary classifier, two useful (but imperfect) criteria are sensitivity and specificity.

Sensitivity is the proportion of truly positives cases that were classified as positive; thus, it is a measure of how well your classifier identifies positive cases.  It is also known as the true positive rate.  Formally,

$\text{Sensitivity} = \text{(Number of True Positives)} \ \div \ \text{(Number of True Positives + Number of False Negatives)}$

Specificity is the proportion of truly negative cases that were classified as negative; thus, it is a measure of how well your classifier identifies negative cases.  It is also known as the true negative rate.  Formally,

$\text{Specificity} = \text{(Number of True Negatives)} \ \div \ \text{(Number of True Negatives + Number of False Positives)}$

## Machine Learning Lesson of the Day – Linear Gaussian Basis Function Models

I recently introduced the use of linear basis function models for supervised learning problems that involve non-linear relationships between the predictors and the target.  A common type of basis function for such models is the Gaussian basis function.  This type of model uses the kernel of the normal (or Gaussian) probability density function (PDF) as the basis function.

$\phi_j(x) = exp[-(x - \mu_j)^2 \div 2\sigma^2]$

The $\sigma$ in this basis function determines the spacing between the different basis functions that combine to form the model.

Notice that this is just the normal PDF without the scaling factor of $1/\sqrt{2\pi \sigma^2}$; the scaling factor ensures that the normal PDF integrates to 1 over its support set.  In a linear basis function model, the regression coefficients are the weights for the basis functions, and these weights will scale Gaussian basis functions to fit the data that are local to $\mu_j$.  Thus, there is no need to include that scaling factor of $1/\sqrt{2\pi \sigma^2}$, because the scaling is already being handled by the regression coefficients.

The Gaussian basis function model is useful because

• it can model many non-linear relationships between the predictor and the target surprisingly well,
• each basis function is non-zero over a very small interval and is zero everywhere else.  These local basis functions result in a very sparse design matrix (i.e. one with mostly zeros) that leads to much faster computation.

## Machine Learning Lesson of the Day – Overfitting and Underfitting

Overfitting occurs when a statistical model or machine learning algorithm captures the noise of the data.  Intuitively, overfitting occurs when the model or the algorithm fits the data too well.  Specifically, overfitting occurs if the model or algorithm shows low bias but high variance.  Overfitting is often a result of an excessively complicated model, and it can be prevented by fitting multiple models and using validation or cross-validation to compare their predictive accuracies on test data.

Underfitting occurs when a statistical model or machine learning algorithm cannot capture the underlying trend of the data.  Intuitively, underfitting occurs when the model or the algorithm does not fit the data well enough.  Specifically, underfitting occurs if the model or algorithm shows low variance but high bias.  Underfitting is often a result of an excessively simple model.

Both overfitting and underfitting lead to poor predictions on new data sets.

In my experience with statistics and machine learning, I don’t encounter underfitting very often.  Data sets that are used for predictive modelling nowadays often come with too many predictors, not too few.  Nonetheless, when building any model in machine learning for predictive modelling, use validation or cross-validation to assess predictive accuracy – whether you are trying to avoid overfitting or underfitting.

## Machine Learning Lesson of the Day – Introduction to Linear Basis Function Models

Given a supervised learning problem of using $p$ inputs ($x_1, x_2, ..., x_p$) to predict a continuous target $Y$, the simplest model to use would be linear regression.  However, what if we know that the relationship between the inputs and the target is non-linear, but we are unsure of exactly what form this relationship has?

One way to overcome this problem is to use linear basis function models.  These models assume that the target is a linear combination of a set of $p+1$ basis functions.

$Y_i = w_0 + w_1 \phi_1(x_1) + w_2 \phi_2(x_2) + ... + w_p \phi_p(x_p)$

This is a generalization of linear regression that essentially replaces each input with a function of the input.  (A linear basis function model that uses the identity function is just linear regression.)

The type of basis functions (i.e. the type of function given by $\phi$) is chosen to suitably model the non-linearity in the relationship between the inputs and the target.  It also needs to be chosen so that the computation is efficient.  I will discuss variations of linear basis function models in a later Machine Learning Lesson of the Day.

## Machine Learning Lesson of the Day – Memory-Based Learning

Memory-based learning (also called instance-based learning) is a type of non-parametric algorithm that compares new test data with training data in order to solve the given machine learning problem.  Such algorithms search for the training data that are most similar to the test data and make predictions based on these similarities.  (From what I have learned, memory-based learning is used for supervised learning only.  Can you think of any memory-based algorithms for unsupervised learning?)

A distinguishing feature of memory-based learning is its storage of the entire training set.  This is computationally costly, especially if the training set is large – the storage itself is costly, and the complexity of the model grows with a larger data set.  However, it is advantageous because it uses less assumptions than parametric models, so it is adaptable to problems for which the assumptions may fail and no clear pattern is known ex ante.  (In contrast, parametric models like linear regression make generalizations about the training data; after building a model to predict the targets, the training data are discarded, so there is no need to store them.)  Thus, I recommend using memory-based learning algorithms when the data set is relatively small and there is no prior knowledge or information about the underlying patterns in the data.

Two classic examples of memory-based learning are K-nearest neighbours classification and K-nearest neighbours regression.

## Machine Learning Lesson of the Day: The K-Nearest Neighbours Classifier

The K-nearest neighbours (KNN) classifier is a non-parametric classification technique that classifies an input $X$ by

1. identifying the K data (the K “neighbours”) in the training set that are closest to $X$
2. counting the number of “neighbours” that belong to each class of the target variable
3. classifying $X$ by the most common class to which its neighbours belong

K is usually an odd number to avoid resolving ties.

The proximity of the neighbours to $X$ is usually defined by Euclidean distance.

Validation or cross-validation can be used to determine the best number of “K”.

## Machine Learning Lesson of the Day – Overfitting

Any model in statistics or machine learning aims to capture the underlying trend or systematic component in a data set.  That underlying trend cannot be precisely captured because of the random variation in the data around that trend.  A model must have enough complexity to capture that trend, but not too much complexity to capture the random variation.  An overly complex model will describe the noise in the data in addition to capturing the underlying trend, and this phenomenon is known as overfitting.

Let’s illustrate overfitting with linear regression as an example.

• A linear regression model with sufficient complexity has just the right number of predictors to capture the underlying trend in the target.  If some new but irrelevant predictors are added to the model, then they “have nothing to do” – all the variation underlying the trend in the target has been captured already.  Since they are now “stuck” in this model, they “start looking” for variation to capture or explain, but the only variation left over is the random noise.  Thus, the new model with these added irrelevant predictors describes the trend and the noise.  It predicts the targets in the training set extremely well, but very poorly for targets in any new, fresh data set – the model captures the noise that is unique to the training set.

(This above explanation used a parametric model for illustration, but overfitting can also occur for non-parametric models.)

To generalize, a model that overfits its training set has low bias but high variance – it predicts the targets in the training set very accurately, but any slight changes to the predictors would result in vastly different predictions for the targets.

Overfitting differs from multicollinearity, which I will explain in later post.  Overfitting has irrelevant predictors, whereas multicollinearity has redundant predictors.

## Machine Learning Lesson of the Day – The “No Free Lunch” Theorem

A model is a simplified representation of reality, and the simplifications are made to discard unnecessary detail and allow us to focus on the aspect of reality that we want to understand.  These simplifications are grounded on assumptions; these assumptions may hold in some situations, but may not hold in other situations.  This implies that a model that explains a certain situation well may fail in another situation.  In both statistics and machine learning, we need to check our assumptions before relying on a model.

The “No Free Lunch” theorem states that there is no one model that works best for every problem.  The assumptions of a great model for one problem may not hold for another problem, so it is common in machine learning to try multiple models and find one that works best for a particular problem.  This is especially true in supervised learning; validation or cross-validation is commonly used to assess the predictive accuracies of multiple models of varying complexity to find the best model.  A model that works well could also be trained by multiple algorithms – for example, linear regression could be trained by the normal equations or by gradient descent.

Depending on the problem, it is important to assess the trade-offs between speed, accuracy, and complexity of different models and algorithms and find a model that works best for that particular problem.

## Machine Learning Lesson of the Day – Cross-Validation

Validation is a good way to assess the predictive accuracy of a supervised learning algorithm, and the rule of thumb of using 70% of the data for training and 30% of the data for validation generally works well.  However, what if the data set is not very large, and the small amount of data for training results in high sampling error?  A good way to overcome this problem is K-fold cross-validation.

Cross-validation is best defined by describing its steps:

For each model under consideration,

1. Divide the data set into K partitions.
2. Designate the first partition as the validation set and designate the other partitions as the training set.
3. Use training set to train the algorithm.
4. Use the validation set to assess the predictive accuracy of the algorithm; the common measure of predictive accuracy is mean squared error.
5. Repeat Steps 2-4 for the second partition, third partition, … , the (K-1)th partition, and the Kth partition.  (Essentially, rotate the designation of validation set through every partition.)
6. Calculate the average of the mean squared error from all K validations.

Compare the average mean squared errors of all models and pick the one with the smallest average mean squared error as the best model.  Test all models on a separate data set (called the test set) to assess their predictive accuracies on new, fresh data.

If there are N data in the data set, and K = N, then this type of K-fold cross-validation has a special name: leave-one-out cross-validation (LOOCV).

There some trade-offs between a large and a small K.  The estimator for the prediction error from a larger K results in

• less bias because of more data being used for training
• higher variance because of the higher similarity and lower diversity between the training sets
• slower computation because of more data being used for training

In The Elements of Statistical Learning (2009 Edition, Chapter 7, Page 241-243), Hastie, Tibshirani and Friedman recommend 5 or 10 for K.

## Machine Learning Lesson of the Day – Parametric vs. Non-Parametric Models

A machine learning algorithm can be classified as either parametric or non-parametric.

A parametric algorithm has a fixed number of parameters.  A parametric algorithm is computationally faster, but makes stronger assumptions about the data; the algorithm may work well if the assumptions turn out to be correct, but it may perform badly if the assumptions are wrong.  A common example of a parametric algorithm is linear regression.

In contrast, a non-parametric algorithm uses a flexible number of parameters, and the number of parameters often grows as it learns from more data.  A non-parametric algorithm is computationally slower, but makes fewer assumptions about the data.  A common example of a non-parametric algorithm is K-nearest neighbour.

To summarize, the trade-offs between parametric and non-parametric algorithms are in computational cost and accuracy.