Week 10 — Classification (10/24–28)#

This week we introduce classification as a prediction task, with methods for evaluating classifiers.

🧐 Content Overview#

🎥 What is Classification?#

In this video, I introduce the week and what classification is.

🎥 Log-Odds and Logistics#

In this video, I introduce log odds, along with the logistic function and its inverse, the logit function. Log odds are a useful concept in many situations!

🎥 Logistic Regression#

We’re now ready for our first classification model: logistic regression.

🎥 The Confusion Matrix#

The confusion matrix describes the outcomes of a classification model and is the basis for computing effectiveness metrics.


  • The Wikipedia article has a very good diagram of the confusion matrix and its derived metrics.

📓 Logistic Regression Demo#

The demo notebook for our initial logistic regression videos.

🎥 Baseline Models#

📃 Floating Point#

This is provided for reference.

📃 StatsModels Documentation#

The following StatsModels page documents its logistic regression:

This is not an assigned reading - it is here for your reference.

🎥 Log Likelihood#

This video describes the log likelihood that is the objective function used by logistic regression.

🎥 Scikit-Learn#

This video introduces SciKit-Learn, and using it for a logistic regression.

📓 SciKit-Learn Logistic Regression#

The SciKit Logistic notebook demonstrates training and using sklearn.linear_model.LogisticRegression.

🎥 Receiver Operating Characteristic#

This video introduces the receiver operating characteristic (ROC) curve, and its use in evaluating classifiers and selecting tradeoffs.

✅ Practice#

Load the Penguin data, and use a logistic regression to try to classify a penguin as Gentoo or Chinstrap using various measurements. Delete the Adelie penguins first, so you have a binary classification problem.

🎥 Biases and Assumptions#

This video revisits sources of bias and discusses the assumptions underlying prediction.

📃 Prediction-Based Decisions#

Read Sections 1 and 2 of the following paper:

Shira Mitchell, Eric Potash, Solon Barocas, Alexander D’Amour, Kristian Lum. 2018. Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions. arXiv:1811.07867 [stat.AP].

We’ll come back to ideas here, but sections 1 and 2 describe the assumptions underlying most classification problems. While the overall topic of the paper is fairness in making these decisions, I am not assigning it because it is a fairness paper; rather, those first two sections provide a succinct description of the assumptions that we make when we undertake most classification problems. They apply no matter what properties of a classification problem or model we care about.

If you would like to learn more, I recommend:

🚩 Week 10 Quiz#

The Week 10 quiz will be posted to Canvas.

📃 Abolish the #TechToPrison Pipeline#

Read Abolish the #TechToPrison Pipeline (the Medium reading time estimate includes the thorough — and valuable — footnotes and list of 2435 signatories). This article probes in more detail the assumptions underlying classes of criminal justice data science applications.

📩 Assignment 5#

Assignment 5 is due November 6.