https://github.com/miozilla/fescarefine

fescarefine :skier::mount_fuji::guide_dog: : Refine & Test ML Models # Features Scaling # Normalization # Datasets
https://github.com/miozilla/fescarefine

gradient-descent linear-regression matplotlib model-testing mse norm numpy ols overfit pandas scatter seaborn sklearn standardization statsmodels underfit

Last synced: 17 days ago
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fescarefine :skier::mount_fuji::guide_dog: : Refine & Test ML Models # Features Scaling # Normalization # Datasets

• Host: GitHub
• URL: https://github.com/miozilla/fescarefine
• Owner: miozilla
• Created: 2025-09-16T05:27:43.000Z (19 days ago)
• Default Branch: main
• Last Pushed: 2025-09-16T05:40:13.000Z (19 days ago)
• Last Synced: 2025-09-16T07:24:00.351Z (19 days ago)
• Topics: gradient-descent, linear-regression, matplotlib, model-testing, mse, norm, numpy, ols, overfit, pandas, scatter, seaborn, sklearn, standardization, statsmodels, underfit
• Language: Jupyter Notebook
• Homepage:
• Size: 2.32 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# fescarefine ⛷️🗻🦮

fescarefine: Refine & Test ML models # Features Scaling # Normalization # Datasets

## Objective

- Define feature scaling/normalization.

- Create and work with test datasets.

- Articulate how testing models can both improve and harm training.

## Scenario: Avalanche Rescue Dogs

- To make the concepts relatable, the module uses a fictional scenario:

- A charity is training dogs to rescue hikers trapped in avalanches.

- There's debate over which traits matter most-size, age, etc.

- Historical rescue data is available to guide training decisions.

- Since training is costly, choosing the right dogs is critical.

- This sets up a classic supervised learning problem: using labeled data (dog traits + rescue outcomes) to build a predictive model.

##  Refine & Test ML models 

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