Sitemap - 2024 - Mindful Modeler
Sneak peek into the new edition of Interpretable Machine Learning
What Are Shapley Interactions, and Why Should You Care?
A place for machine learning conversations
We are obsessed with benchmarks
What is Mechanistic Interpretability and where did it come from?
🐦⬛ Supervised Machine Learning for Science is published 🥳
How to implement permutation feature importance
No learning without randomness
One model or many? Balancing entity-specific effects in prediction tasks
Book Review: Why Machines Learn
Can you draw scientific conclusions with interpretable machine learning?
A simple recipe for model error analysis
Fixing Bias: How Error Analysis Improved My Water Forecasting Model
The rise of self-supervised learning
From Datasheets to Model Cards
Why it's hard to make machine learning reproducible
Rethinking Machine Learning: The Role of Similarity
The Random Forest is not a Random Forest
Shedding light on "Impossibility Theorems for Feature Attribution"
Inductive biases - a better way to think about machine learning?
How to make use of inductive biases
Statistical modeling seen through inductive biases
Inductive biases of the Random Forest and their consequences
Ignore inductive biases at your own peril
From Theory to Practice: Inductive Biases in Machine Learning
A new chapter on generalization
7 perspectives on machine learning
Embrace the clash between domain expertise and machine learning
How to get from evaluation to final model
My perfectly imperfect note-taking system for ML papers
Machine learning algorithms to live by
Machine learning never cheats but it may play flawed games
No Free Dessert in Machine Learning
Book Launch: ML for Science 🐦⬛
Machine Learning's Secret Sauce: Competition
How to sell bread with quantile regression