Explores the evolution from simple logistic regression to modern AI, comparing model complexity, data requirements, and the surprising effectiveness of large neural networks.
Explores the challenges of fitting a logistic curve using only data from its early, exponential-like growth phase, highlighting the unreliability of such extrapolations.
A guide to manually generating predicted values for logistic regression using matrix multiplication in R, as an alternative to the predict() function.
Explains the proportional odds model for ordinal data, its assumptions, and discusses methods for testing the proportionality of odds.
A data scientist details the complex process of tracing the original source and context of a medical dataset used in statistical software packages.
Explores the logit-normal distribution, its mathematical properties, and its surprising role in statistical models like logistic regression.
A review and tutorial on interpretable machine learning, covering Christoph Molnar's book and providing Python code examples for linear/logistic regression.
A review and tutorial covering Christoph Molnar's book on Interpretable Machine Learning, with Python code examples for linear and logistic regression.
Explains how prior probabilities are learned and updated in logistic regression models, using a coffee brewing example to illustrate class imbalance.
Explains the mathematical derivation of logistic regression from Bayes' theorem, connecting fundamental statistics to machine learning.
Analyzing tweet sentiment towards public figures using R, word embeddings, and logistic regression models to measure online negativity.
Explores optimal sampling design for logistic regression in case-control studies, analyzing Neyman allocation and two-phase sampling variances.
A guide to implementing logistic regression with gradient descent in JavaScript to solve classification problems.
Analyzes efficiency differences between weighted and unweighted logistic regression in case-control studies, showing when ignoring weights is beneficial.
Explores defining and computing design-based pseudo-R-squared statistics for logistic regression models under complex survey sampling, like case-control designs.
Explores the complexities and efficiency trade-offs between weighted and unweighted logistic regression in case-control study designs.
Analyzes semiparametric efficiency in two-phase sampling designs, comparing estimators under correctly specified and 'nearly true' models.
Explores using Principal Component Analysis on t-shirt images to build a gender classification model, visualizing data as 'eigenshirts'.
