Model-evaluation - TechnoScripts

model-evaluation

Guides on evaluating machine learning models with metrics, validation, and test sets.

Model Drift in Python: Detect and Fix Decaying ML Models

Model drift is what happens when a machine learning model that scored 94% on launch day quietly slides to 70% six months later, without a single line of its code changing. The world moved, and the model stayed frozen in the past. This tutorial simulates that decay week by week, catches it early with Evidently, and wires up a monitoring loop that alerts and retrains before your users feel it. “A model is a photograph of the world on the ... Read More

Machine Learning Interview Questions: 40-Question Checkpoint

Most ML screens recycle the same forty or so theory fundamentals, just reworded. That is good news: the list is learnable. This checkpoint collects the machine learning interview questions that actually come up, grouped the way real screens group them, each with a spoken-style answer and runnable code that proves the claims people get wrong. Tally your score at the end and turn every miss into a short revisit list. “A model is only as trustworthy as the way you ... Read More

Your First Kaggle Competition: An ML Project That Counts

This Kaggle project walks you through a real getting-started competition from the first look at the data to a submission file you upload for a live leaderboard score. You will explore the data, split it the right way, build an honest baseline, add features, cross-validate, train one boosted model, and dodge the two data leaks that quietly wreck most first attempts. Everything runs on a laptop in seconds, no Graphics Processing Unit (GPU) and no paid account. “The public leaderboard ... Read More