An Interactive Spreadsheet Model for Teaching Classification Using Logistic Regression
Why this work is in the frame
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Bibliographic record
Abstract
We present an interactive spreadsheet that supports teaching essential concepts in classification using the logistic regression (LoR) model for binary classification. The interactive spreadsheet demonstrates the capabilities of LoR by integrating computation with visualization. Students will reinforce concepts like probabilities, maximum likelihood estimation (MLE), and the use of likelihoods to optimize parameters for the LoR. We then discuss using LoR for classifications while adjusting its decision boundary (DB), demonstrating how to convert assigned likelihoods into classification using the DB; impact classification outcome by varying DBs; designate predictions as true positive, true negative, false positive, or false negative; and determine the classification accuracy. We use a variety of performance measures, including sensitivity, specificity, precision, negative predictive value, F 1 and F 2 scores, the receiver operating characteristics curve, and lift/decile charts. These measures are dynamically adjusted when the DB changes. We also reiterate the usage of these measures in the context of crossvalidation and imbalanced data sets. We provide a case study that implements LoR and an option for teaching the details behind MLE. We discuss the pedagogical aspects of this spreadsheet based on a survey of the 2022 student cohort in the Master of Management Analytics Program at the Rotman School of Management.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it