A collection of blog posts about scikit learn
Can an AI guess which data matters without ever seeing the data? A small experiment pits a language model against two standard methods on 1994 census data, with the full notebook to run yourself.
Scikit-Learn is the workhorse of applied machine learning in Python. Here's what it does, why it's worth learning deeply, and a practical example to get you started.
Both entropy and Gini impurity measure how mixed a node is in a decision tree — but they differ in computation, behavior, and when each makes sense to use.
Both Lasso and Ridge add a penalty to linear models to prevent overfitting — but they behave very differently. Here's when to use each and how to implement both.
The Titanic dataset is the classic ML classification benchmark. Here's a full walkthrough — feature engineering, Random Forest, Logistic Regression, and what the results actually mean.
Grid Search exhaustively evaluates every combination in a parameter grid using cross-validation. Here's how it works, when to use it, and a complete implementation with Scikit-Learn.
Analytics engineer turning fragmented tidbits into confidence: messy data structured, patterns caught early, answers made plain with AI.
Copyright 2026 Christopher A. Rotunno. All Rights Reserved
