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How to get from evaluation to final model
We’ve all been there: You’ve set up a machine learning pipeline with tuning, model selection, and evaluation. Tons of data splitting, maybe with…
Mar 19
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Christoph Molnar
11
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How to get from evaluation to final model
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4
My perfectly imperfect note-taking system for ML papers
A note-taking system based on Obsidian and Zotero
Mar 12
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Christoph Molnar
36
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My perfectly imperfect note-taking system for ML papers
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3
Machine learning algorithms to live by
The theories you learn shape how you see the world. A lawyer has the legal perspective in mind, an entrepreneur smells business opportunities…
Mar 5
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Christoph Molnar
25
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Machine learning algorithms to live by
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February 2024
Machine learning never cheats but it may play flawed games
How to become better at designing prediction games
Feb 27
•
Christoph Molnar
13
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Machine learning never cheats but it may play flawed games
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3
No Free Dessert in Machine Learning
Generalization in machine learning is too narrow. If you want more, you have to give more.
Feb 20
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Christoph Molnar
19
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No Free Dessert in Machine Learning
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to kaggle, or not to kaggle
Ultimately, it depends on Bob from IT Services.
Feb 13
•
Christoph Molnar
17
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to kaggle, or not to kaggle
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Book Launch: ML for Science 🐦⬛
A philosophical and pragmatic justification of supervised machine learning in science that you can read for free
Feb 6
•
Christoph Molnar
23
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Book Launch: ML for Science 🐦⬛
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January 2024
Machine Learning's Secret Sauce: Competition
What machine learning has to do with marathons, the Fosbury Flop, and running a sub-4-minute mile.
Jan 30
•
Christoph Molnar
11
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Machine Learning's Secret Sauce: Competition
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How to sell bread with quantile regression
Practical tips for when and how to use quantile regression in machine learning
Jan 23
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Christoph Molnar
17
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How to sell bread with quantile regression
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5
How I made peace with quantile regression
Understanding quantile regression through the lens of loss optimization
Jan 16
•
Christoph Molnar
38
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How I made peace with quantile regression
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3
How to deal with non-i.i.d data in machine learning
Ignoring this common problem can lead to data leakage, overfitting, and overly optimistic evaluation.
Jan 9
•
Christoph Molnar
24
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How to deal with non-i.i.d data in machine learning
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December 2023
Use Interpretability to Justify Your Model
If a machine learning model performs well, it’s justification enough for using that model. Or so some people claim. But in reality, performance is…
Dec 19, 2023
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Christoph Molnar
10
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Use Interpretability to Justify Your Model
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