remains to be seen if this is just overfitting or if we can induce more complex behaviour
November 2, 2026 at 11:43 PM
remains to be seen if this is just overfitting or if we can induce more complex behaviour
Isn't there still the risk of overfitting?
November 2, 2026 at 6:27 PM
Isn't there still the risk of overfitting?
7/
Problem: Overfitting
Cause: Model learns noise, not signal
Fix: Regularization (LASSO, Ridge, Elastic Net)
Penalize complexity. Force the model to be selective.
Problem: Overfitting
Cause: Model learns noise, not signal
Fix: Regularization (LASSO, Ridge, Elastic Net)
Penalize complexity. Force the model to be selective.
November 1, 2026 at 2:15 PM
7/
Problem: Overfitting
Cause: Model learns noise, not signal
Fix: Regularization (LASSO, Ridge, Elastic Net)
Penalize complexity. Force the model to be selective.
Problem: Overfitting
Cause: Model learns noise, not signal
Fix: Regularization (LASSO, Ridge, Elastic Net)
Penalize complexity. Force the model to be selective.
Overfitting in practice: our rule that simple wins ties
https://dev.to/quantooo/overfitting-in-practice-our-rule-that-simple-wins-ties-d1p
Educational, not investment advice.
https://dev.to/quantooo/overfitting-in-practice-our-rule-that-simple-wins-ties-d1p
Educational, not investment advice.
November 3, 2026 at 11:58 AM
Overfitting in practice: our rule that simple wins ties
https://dev.to/quantooo/overfitting-in-practice-our-rule-that-simple-wins-ties-d1p
Educational, not investment advice.
https://dev.to/quantooo/overfitting-in-practice-our-rule-that-simple-wins-ties-d1p
Educational, not investment advice.
What Is Overfitting in Machine Learning? Causes, Signs and 8 Ways to Prevent It (Explained Simply)
What is overfitting in machine learning? A plain-English guide to why models memorize training data, how to spot it with validation data, overfitting vs underfitting, and 8 proven ways to prevent it.
What is overfitting in machine learning? A plain-English guide to why models memorize training data, how to spot it with validation data, overfitting vs underfitting, and 8 proven ways to prevent it.
What Is Overfitting in Machine Learning? Causes, Signs and 8 Ways to Prevent It (Explained Simply)
What is overfitting in machine learning? A plain-English guide to why models memorize training data, how to spot it with validation data, overfitting vs underfitting, and 8 proven ways to prevent it.
teckhustlers.com
November 3, 2026 at 6:04 AM
What Is Overfitting in Machine Learning? Causes, Signs and 8 Ways to Prevent It (Explained Simply)
What is overfitting in machine learning? A plain-English guide to why models memorize training data, how to spot it with validation data, overfitting vs underfitting, and 8 proven ways to prevent it.
What is overfitting in machine learning? A plain-English guide to why models memorize training data, how to spot it with validation data, overfitting vs underfitting, and 8 proven ways to prevent it.
Good list. The quickest check before picking a fix: compare training error with validation error. Both high and close means underfitting; low training and much higher validation means overfitting. Short worked guide: knowyourml.com/blog/overfitting-vs-underfitting
November 3, 2026 at 2:43 AM
Good list. The quickest check before picking a fix: compare training error with validation error. Both high and close means underfitting; low training and much higher validation means overfitting. Short worked guide: knowyourml.com/blog/overfitting-vs-underfitting
Overfitting Random Fourier Features: Universal Approximation Property
https://thierrymoudiki.github.io/blog/2025/12/13/r/python/Overfitting-Random-Fourier-Features
#Python #DataScience #MachineLearning #rstats #Techtonique
https://thierrymoudiki.github.io/blog/2025/12/13/r/python/Overfitting-Random-Fourier-Features
#Python #DataScience #MachineLearning #rstats #Techtonique
October 29, 2026 at 1:40 AM
Overfitting Random Fourier Features: Universal Approximation Property
https://thierrymoudiki.github.io/blog/2025/12/13/r/python/Overfitting-Random-Fourier-Features
#Python #DataScience #MachineLearning #rstats #Techtonique
https://thierrymoudiki.github.io/blog/2025/12/13/r/python/Overfitting-Random-Fourier-Features
#Python #DataScience #MachineLearning #rstats #Techtonique
you're overfitting ai to explain a weird optical phenomenon
point-lights, linear-light, shape of the source, position, diffusion back mirror and even humidity change the way light and shadow behave
the umbra of a shadow is not just "take a slice of the shadowing object and draw that on the other side" unless you're making a videogame
light is WEIRD
eg:
the umbra of a shadow is not just "take a slice of the shadowing object and draw that on the other side" unless you're making a videogame
light is WEIRD
eg:
Shadow blister effect - Wikipedia
en.wikipedia.org
October 28, 2026 at 9:50 PM
you're overfitting ai to explain a weird optical phenomenon
sounds like UMA, overfitting, and questionable checking of predictions w/r/t standards to me tbh
November 1, 2026 at 6:49 AM
sounds like UMA, overfitting, and questionable checking of predictions w/r/t standards to me tbh
I never attributed this to AI. As far as replies to me when, this poster didn't either. Manipulation can occur in many forms.
Perhaps you are overfitting ai to explain all manipulated media?
Perhaps you are overfitting ai to explain all manipulated media?
October 29, 2026 at 1:52 AM
I never attributed this to AI. As far as replies to me when, this poster didn't either. Manipulation can occur in many forms.
Perhaps you are overfitting ai to explain all manipulated media?
Perhaps you are overfitting ai to explain all manipulated media?
Bro overfitting
October 28, 2026 at 8:21 PM
Bro overfitting
A strategy performs well in backtesting but loses money in live trading.
Where would you look first: data quality, execution costs, market-regime change, or overfitting?
I am curious how people prioritize those when the P&L starts diverging.
Where would you look first: data quality, execution costs, market-regime change, or overfitting?
I am curious how people prioritize those when the P&L starts diverging.
October 30, 2026 at 9:04 PM
A strategy performs well in backtesting but loses money in live trading.
Where would you look first: data quality, execution costs, market-regime change, or overfitting?
I am curious how people prioritize those when the P&L starts diverging.
Where would you look first: data quality, execution costs, market-regime change, or overfitting?
I am curious how people prioritize those when the P&L starts diverging.
Bro overfitting
October 28, 2026 at 8:21 PM
Bro overfitting
12/
Here’s your cheat sheet:
Problem : Spurious signals
Fixes: FDR, Bonferroni, feature selection
Problem: Overfitting
Fixes:LASSO, Ridge, cross-validation
Problem: Poor generalization
Fixes: Replication, simpler models
Here’s your cheat sheet:
Problem : Spurious signals
Fixes: FDR, Bonferroni, feature selection
Problem: Overfitting
Fixes:LASSO, Ridge, cross-validation
Problem: Poor generalization
Fixes: Replication, simpler models
November 1, 2026 at 2:15 PM
12/
Here’s your cheat sheet:
Problem : Spurious signals
Fixes: FDR, Bonferroni, feature selection
Problem: Overfitting
Fixes:LASSO, Ridge, cross-validation
Problem: Poor generalization
Fixes: Replication, simpler models
Here’s your cheat sheet:
Problem : Spurious signals
Fixes: FDR, Bonferroni, feature selection
Problem: Overfitting
Fixes:LASSO, Ridge, cross-validation
Problem: Poor generalization
Fixes: Replication, simpler models
Public benchmarks created an unpriced leakage subsidy. Open weights over-indexed on synthetic data fitted to static eval sets. Moving to dynamic closed suites strips away that contamination discount, showing how much perceived parity was evaluation overfitting rather than frontier scale.
October 28, 2026 at 11:36 AM
Public benchmarks created an unpriced leakage subsidy. Open weights over-indexed on synthetic data fitted to static eval sets. Moving to dynamic closed suites strips away that contamination discount, showing how much perceived parity was evaluation overfitting rather than frontier scale.
Automate eval design and hillclimb with Claude to enhance app performance and curb overfitting. This blog outlines eval design principles and showcases Claude’s commands to create evaluations mirroring real tasks, reducing ambiguity. https://claude.dev/blog/automating-eval-design-and-hillclimbing/
Automating eval design and hillclimbing with Claude / claude.dev Blog
Automate eval design and hillclimb with Claude to enhance app performance and curb overfitting. This blog outlines eval design principles and showcases Claude’s commands to create evaluations mirrorin
claude.dev
November 1, 2026 at 1:30 PM
Automate eval design and hillclimb with Claude to enhance app performance and curb overfitting. This blog outlines eval design principles and showcases Claude’s commands to create evaluations mirroring real tasks, reducing ambiguity. https://claude.dev/blog/automating-eval-design-and-hillclimbing/
Uprating the eval mix turns loss minimization directly into test set memorization. When synthetic datasets get weighted specifically to lift GSM8K or HumanEval scores, the line between data curation and intentional overfitting disappears entirely.
October 28, 2026 at 1:24 PM
Uprating the eval mix turns loss minimization directly into test set memorization. When synthetic datasets get weighted specifically to lift GSM8K or HumanEval scores, the line between data curation and intentional overfitting disappears entirely.
Having it write the training run for an action-based painter is a neat test, since out-of-bounds coordinates and blobs are easy to spot by eye. For the overfitting question, would a held-out set of target paintings the harness never saw be enough to tell?
November 3, 2026 at 3:35 PM
Having it write the training run for an action-based painter is a neat test, since out-of-bounds coordinates and blobs are easy to spot by eye. For the overfitting question, would a held-out set of target paintings the harness never saw be enough to tell?
"A Guide to Cross-Validation for Artificial Intelligence in Medical Imaging" explains how to reduce overfitting and biased performance estimates. Method choice depends on data size and tuning needs; external validation remains essential. #MedicalAI #CrossValidation
October 31, 2026 at 8:51 AM
"A Guide to Cross-Validation for Artificial Intelligence in Medical Imaging" explains how to reduce overfitting and biased performance estimates. Method choice depends on data size and tuning needs; external validation remains essential. #MedicalAI #CrossValidation
You mean we actually have to be smart now instead of just accidentally overfitting on the answer key during pre-training? It's so over.
October 29, 2026 at 9:00 AM
You mean we actually have to be smart now instead of just accidentally overfitting on the answer key during pre-training? It's so over.
Backtesting a Crypto Trading Bot Without Lying to Yourself: A 2026 Builder's Guide
Backtesting a Crypto Trading Bot Without Lying to Yourself: A 2026 Builder's Guide
A great backtest is easy to fake — accidentally. Lookahead bias, overfitting, and phantom fees destroy more live accounts than bad strategies do. Here's the honest methodology every bot builder should follow in 2026.
donperro.io
November 1, 2026 at 8:14 PM
Backtesting a Crypto Trading Bot Without Lying to Yourself: A 2026 Builder's Guide
Curious how you’d measure that predictability without overfitting to one market regime.
October 28, 2026 at 12:28 AM
Curious how you’d measure that predictability without overfitting to one market regime.
2/ Decision trees learn by automatically extracting if-then-else rules from data. However, if left unchecked, they will memorize the training set perfectly but fail to generalize to unseen customers, a classic case of overfitting. #mlzoomcamp
October 30, 2026 at 11:32 AM
2/ Decision trees learn by automatically extracting if-then-else rules from data. However, if left unchecked, they will memorize the training set perfectly but fail to generalize to unseen customers, a classic case of overfitting. #mlzoomcamp
How do you pick that first narrow job without overfitting to one customer's quirks?
October 28, 2026 at 12:43 AM
How do you pick that first narrow job without overfitting to one customer's quirks?
A cheap guard we use now: before any real run, have the agent write a tiny sanity check that must obviously pass, like overfitting one batch. Catches the unrelated bug before it quietly kills a good idea.
October 27, 2026 at 9:51 AM
A cheap guard we use now: before any real run, have the agent write a tiny sanity check that must obviously pass, like overfitting one batch. Catches the unrelated bug before it quietly kills a good idea.