Machine Learning

Supervised vs unsupervised vs reinforcement learning is the first big map you need when learning machine learning: three families of

Sklearn logistic regression is the fastest way to train a working classifier in Python: with scikit-learn you can import one

The logistic regression decision boundary is the exact place where the model stops favoring one class and starts favoring the

Logistic regression gradient descent is the iterative procedure that actually trains a logistic regression model — it starts with random

This logistic regression example walks through a complete classification problem from raw data to a final prediction, using the friendliest

The logistic regression assumptions are the short list of conditions your data must satisfy for the model’s coefficients, p-values, and

Ordinal logistic regression is the model you reach for when your outcome has three or more ordered categories — low/medium/high,

Multinomial logistic regression is the model you reach for when the outcome has three or more unordered categories — like

Binary logistic regression is the standard machine learning model for predicting a two-class outcome — yes/no, pass/fail, spam/not spam —

Multivariate logistic regression is logistic regression with several predictors feeding a single yes/no outcome, and it is the everyday workhorse