Tag: machinelearning

ROC, AUC, precision, and recall visually explained

ROC, AUC, precision, and recall visually explained

A receiver operating characteristic (ROC) curve displays how well a model can classify binary outcomes. An ROC curve is generated by plotting the false positive rate of a model against its true positive rate, for each possible cutoff value. Often, the area under the curve (AUC) is calculated and used as a metric showing how well a model can classify data points.

If you’re interest in learning more about ROC and AUC, I recommend this short Medium blog, which contains this neat graphic:

Dariya Sydykova, graduate student at the Wilke lab at the University of Texas at Austin, shared some great visual animations of how model accuracy and model cutoffs alter the ROC curve and the AUC metric. The quotes and animations are from the associated github repository.

ROC & AUC

The plot on the left shows the distributions of predictors for the two outcomes, and the plot on the right shows the ROC curve for these distributions. The vertical line that travels left-to-right is the cutoff value. The red dot that travels along the ROC curve corresponds to the false positive rate and the true positive rate for the cutoff value given in the plot on the left.

The traveling cutoff demonstrates the trade-off between trying to classify one outcome correctly and trying to classify the other outcome correcly. When we try to increase the true positive rate, we also increase the false positive rate. When we try to decrease the false positive rate, we decrease the true positive rate.

cutoff.gif

The shape of an ROC curve changes when a model changes the way it classifies the two outcomes.

The animation [below] starts with a model that cannot tell one outcome from the other, and the two distributions completely overlap (essentially a random classifier). As the two distributions separate, the ROC curve approaches the left-top corner, and the AUC value of the curve increases. When the model can perfectly separate the two outcomes, the ROC curve forms a right angle and the AUC becomes 1.

Precision-Recall

Two other metrics that are often used to quantify model performance are precision and recall.

Precision (also called positive predictive value) is defined as the number of true positives divided by the total number of positive predictions. Hence, precision quantifies what percentage of the positive predictions were correct: How correct your model’s positive predictions were.

Recall (also called sensitivity) is defined as the number of true positives divided by the total number of true postives and false negatives (i.e. all actual positives). Hence, recall quantifies what percentage of the actual positives you were able to identify: How sensitive your model was in identifying positives.

Dariya also made some visualizations of precision-recall curves:

Precision-recall curves also displays how well a model can classify binary outcomes. However, it does it differently from the way an ROC curve does. Precision-recall curve plots true positive rate (recall or sensitivity) against the positive predictive value (precision). 

In the middle, here below, the ROC curve with AUC. On the right, the associated precision-recall curve.

Similarly to the ROC curve, when the two outcomes separate, precision-recall curves will approach the top-right corner. Typically, a model that produces a precision-recall curve that is closer to the top-right corner is better than a model that produces a precision-recall curve that is skewed towards the bottom of the plot.

Class imbalance

Class imbalance happens when the number of outputs in one class is different from the number of outputs in another class. For example, one of the distributions has 1000 observations and the other has 10. An ROC curve tends to be more robust to class imbalanace that a precision-recall curve. 

In this animation [below], both distributions start with 1000 outcomes. The blue one is then reduced to 50. The precision-recall curve changes shape more drastically than the ROC curve, and the AUC value mostly stays the same. We also observe this behaviour when the other disribution is reduced to 50. 

Here’s the same, but now with the red distribution shrinking to just 50 samples.

Dariya invites you to use these visualizations for educational purposes:

Please feel free to use the animations and scripts in this repository for teaching or learning. You can directly download the gif files for any of the animations, or you can recreate them using these scripts. Each script is named according to the animation it generates (i.e. animate_ROC.r generates ROC.gif, animate_SD.r generates SD.gif, etc.).

Want to learn more about the different evaluation metrics for machine learning? Here’s a nice how-to guide by Neptune.ai demonstrating different metrics applied in Python.

Artificial Stupidity – by Vincent Warmerdam @PyData 2019 London

Artificial Stupidity – by Vincent Warmerdam @PyData 2019 London

PyData is famous for it’s great talks on machine learning topics. This 2019 London edition, Vincent Warmerdam again managed to give a super inspiring presentation. This year he covers what he dubs Artificial Stupidity™. You should definitely watch the talk, which includes some great visual aids, but here are my main takeaways:

Vincent speaks of Artificial Stupidity, of machine learning gone HorriblyWrong™ — an example of which below — for which Vincent elaborates on three potential fixes:

Image result for paypal but still learning got scammed
Example of a model that goes HorriblyWrong™, according to Vincent’s talk.

1. Predict Less, but Carefully

Vincent argues you shouldn’t extrapolate your predictions outside of your observed sampling space. Even better: “Not predicting given uncertainty is a great idea.” As an alternative, we could for instance design a fallback mechanism, by including an outlier detection model as the first step of your machine learning model pipeline and only predict for non-outliers.

I definately recommend you watch this specific section of Vincent’s talk because he gives some very visual and intuitive explanations of how extrapolation may go HorriblyWrong™.

Be careful! One thing we should maybe start talking about to our bosses: Algorithms merely automate, approximate, and interpolate. It’s the extrapolation that is actually kind of dangerous.

Vincent Warmerdam @ Pydata 2019 London

Basically, we can choose to not make automated decisions sometimes.

2. Constrain thy Features

What we feed to our models really matters. […] You should probably do something to the data going into your model if you want your model to have any sort of fairness garantuees.

Vincent Warmerdam @ Pydata 2019 London

Often, simply removing biased features from your data does not reduce bias to the extent we may have hoped. Fortunately, Vincent demonstrates how to remove biased information from your variables by applying some cool math tricks.

Unfortunately, doing so will often result in a lesser predictive accuracy. Unsurprisingly though, as you are not closely fitting the biased data any more. What makes matters more problematic, Vincent rightfully mentions, is that corporate incentives often not really align here. It might feel that you need to pick: it’s either more accuracy or it’s more fairness.

However, there’s a nice solution that builds on point 1. We can now take the highly accurate model and the highly fair model, make predictions with both, and when these predictions differ, that’s a very good proxy where you potentially don’t want to make a prediction. Hence, there may be observations/samples where we are comfortable in making a fair prediction, whereas in most other situations we may say “right, this prediction seems unfair, we need a fallback mechanism, a human being should look at this and we should not automate this decision”.

Vincent does not that this is only one trick to constrain your model for fairness, and that fairness may often only be fair in the eyes of the beholder. Moreover, in order to correct for these biases and unfairness, you need to know about these unfair biases. Although outside of the scope of this specific topic, Vincent proposes this introduces new ethical issues:

Basically, we can choose to put our models on a controlled diet.

3. Constrain thy Model

Vincent argues that we should include constraints (based on domain knowledge, or common sense) into our models. In his presentation, he names a few. For instance, monotonicity, which implies that the relationship between X and Y should always be either entirely non-increasing, or entirely non-decreasing. Incorporating the previously discussed fairness principles would be a second example, and there are many more.

If we every come up with a model where more smoking leads to better health, that’s bad. I have enough domain knowledge to say that that should never happen. So maybe I should just make a system where I can say “look this one column with relationship to Y should always be strictly negative”.

Vincent Warmerdam @ Pydata 2019 London

Basically, we can integrate domain knowledge or preferences into our models.

Conclusion: Watch the talk!

Northstar: The interactive, drag-and-drop data science platform by MIT

Northstar: The interactive, drag-and-drop data science platform by MIT

MIT researchers have spent years developing the new drag-and-drop analytics tools they call Northstar.

Northstar is an interactive data science platform that rethinks how people interact with data. It empowers users without programming experience, background in statistics or machine learning expertise to explore and mine data through an intuitive user interface, and effortlessly build, analyze, and evaluate machine learning (ML) pipelines.

northstar.mit.edu/

Northstar starts as a blank, white interface. Users upload datasets into the system, which appear in a “datasets” box on the left. Any data labels will automatically populate a separate “attributes” box below. There’s also an “operators” box that contains various algorithms, as well as the new AutoML tool. All data are stored and analyzed in the cloud.

news.mit.edu/2019/drag-drop-data-analytics-0627

You can read more about the tool’s functionalities in this MIT news article, which includes several promising GIFs:

Moreover, on the Northstar website you can find this longer video explaining the tool in detail.

https://vimeo.com/342787403

While Northstar looks insanely cool and promising, I do worry about putting such power in the hands of people who may not have much experience with statistics and/or machine learning. We all know how easily errors and bias may slip into data-driven processes, so I am curious to see how these next-gen kind of tools will be deployed and used.

Survival of the Best Fit: A webgame on AI in recruitment

Survival of the Best Fit: A webgame on AI in recruitment

Survival of the Best Fit is a webgame that simulates what happens when companies automate their recruitment and selection processes.

You – playing as the CEO of a starting tech company – are asked to select your favorite candidates from a line-up, based on their resumés.

As your simulated company grows, the time pressure increases, and you are forced to automate the selection process.

Fortunately, some smart techies working for your company propose training a computer to hire just like you just did.

They don’t need anything but the data you just generated and some good old supervised machine learning!

To avoid spoilers, try the game yourself and see what happens!

The game only takes a few minutes, and is best played on mobile.

www.survivalofthebestfit.com/ via Medium

Survival of the Best Fit was built by Gabor Csapo, Jihyun Kim, Miha Klasinc, and Alia ElKattan. They are software engineers, designers and technologists, advocating for better software that allows members of the public to question its impact on society.

You don’t need to be an engineer to question how technology is affecting our lives. The goal is not for everyone to be a data scientist or machine learning engineer, though the field can certainly use more diversity, but to have enough awareness to join the conversation and ask important questions.

With Survival of the Best Fit, we want to reach an audience that may not be the makers of the very technology that impact them everyday. We want to help them better understand how AI works and how it may affect them, so that they can better demand transparency and accountability in systems that make more and more decisions for us.

survivalofthebestfit.com

I found that the game provides a great intuitive explanation of how (humas) bias can slip into A.I. or machine learning applications in recruitment, selection, or other human resource management practices and processes.

If you want to read more about people analytics and machine learning in HR, I wrote my dissertation on the topic and have many great books I strongly recommend.

Finally, here’s a nice Medium post about the game.

https://www.survivalofthebestfit.com/game/

Note, as Joachin replied below, that the game apparently does not learn from user-input, but is programmed to always result in bias towards blues.
I kind of hoped that there was actually an algorithm “learning” in the backend, and while the developers could argue that the bias arises from the added external training data (you picked either Google, Apple, or Amazon to learn from), it feels like a bit of a disappointment that there is no real interactivity here.

StatQuest: Statistical concepts, clearly explained

StatQuest: Statistical concepts, clearly explained

Josh Starmer is assistant professor at the genetics department of the University of North Carolina at Chapel Hill.

But more importantly:
Josh is the mastermind behind StatQuest!

StatQuest is a Youtube channel (and website) dedicated to explaining complex statistical concepts — like data distributions, probability, or novel machine learning algorithms — in simple terms.

Once you watch one of Josh’s “Stat-Quests”, you immediately recognize the effort he put into this project. Using great visuals, a just-about-right pace, and relateable examples, Josh makes statistics accessible to everyone. For instance, take this series on logistic regression:

And do you really know what happens under the hood when you run a principal component analysis? After this video you will:

Or are you more interested in learning the fundamental concepts behind machine learning, then Josh has some videos for you, for instance on bias and variance or gradient descent:

With nearly 200 videos and counting, StatQuest is truly an amazing resource for students ‘and teachers on topics related to statistics and data analytics. For some of the concepts, Josh even posted videos running you through the analysis steps and results interpretation in the R language.

StatQuest started out as an attempt to explain statistics to my co-workers – who are all genetics researchers at UNC-Chapel Hill. They did these amazing experiments, but they didn’t always know what to do with the data they generated. That was my job. But I wanted them to understand that what I do isn’t magic – it’s actually quite simple. It only seems hard because it’s all wrapped up in confusing terminology and typically communicated using equations. I found that if I stripped away the terminology and communicated the concepts using pictures, it became easy to understand.

Over time I made more and more StatQuests and now it’s my passion on YouTube.

Josh Starmer via https://statquest.org/about/

Free Programming Books (I still need to read)

Free Programming Books (I still need to read)

There are multiple unread e-mails in my inbox.

Links to books.

Just sitting there. Waiting to be opened, read. For months already.

The sender, you ask? Me. Paul van der Laken.

A nuisance that guy, I tell you. He keeps sending me reminders, of stuff to do, books to read. Books he’s sure a more productive me would enjoy.

Now, I could wipe my inbox. Be done with it. But I don’t wan’t to lose this digital to-do list… Perhaps I should put them here instead. So you can help me read them!

Each of the below links represents a formidable book on programming! (I hear)
And there are free versions! Have a quick peek. A peek won’t hurt you:

Disclaimer: This page contains one or more links to Amazon.
Any purchases made through those links provide us with a small commission that helps to host this blog.

The books listed above have a publicly accessible version linked. Some are legitimate. Other links are somewhat shady.
If you feel like you learned something from reading one of the books (which you surely will), please buy a hardcopy version. Or an e-book. At the very least, reach out to the author and share what you appreciated in his/her work.
It takes valuable time to write a book, and we should encourage and cherish those who take that time.

For more books on R programming, check out my R resources overview.

For books on data analytics and (behavioural) psychology in (HR) management, check out Books for the modern data-driven HR professional.