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BSBerkan Sesensesenai.hashnode.dev1d ago · 13 min read

AIC and BIC: Choosing the Right Model Without Overfitting

Imagine you're fitting a curve to noisy data. A straight line misses the shape entirely, so you try a quadratic, then a cubic, then keep going. By degree 10 the curve passes through nearly every point

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ASAnton Sarokaanton-saroka.hashnode.devJun 20 · 9 min read

CADE — An Interesting Approach to Finding Anomalies in Multidimensional Data

This is a translation of my original article on habr.com. Introduction One way to search for anomalies in a dataset is to use the probability density function corresponding to the data as a measure of

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ASAnton Sarokaanton-saroka.hashnode.devJun 20 · 7 min read

What Is the Distribution of Sample Quantiles?

This is a translation of my original article on habr.com. Sample means, sample variances, sample quantiles, and other statistics are random variables by nature. Knowing their distributions helps us bu

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Mmayaanderssonllmasajudge.hashnode.devJun 16 · 3 min read

Stratified sampling for LLM eval sets: why your aggregate pass rate hides the regressions that matter

TL;DR: A headline eval pass rate is an average over every kind of input your system sees, and averages hide the thing you most need to catch: a sharp regression in a small but important slice. If refu

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BSBerkan Sesensesenai.hashnode.devJun 12 · 13 min read

LDA vs PCA: Supervised Meets Unsupervised Dimensionality Reduction

You have a high-dimensional dataset and you need to squeeze it down to two or three dimensions for visualisation or downstream modelling. The go-to move is PCA, and most of the time it works. But cons

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AAdarshaasteriskz.hashnode.devJun 11 · 11 min read

Building an Autonomous Monte Carlo Engine to Predict the 2026 World Cup

Today is the start of the 2026 FIFA World Cup, the largest sporting competition every four years. As a fun project, I decided to build a model to predict the tournament. With sports, you never really

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Mmayaanderssonllmasajudge.hashnode.devJun 11 · 2 min read

We put confidence intervals on our LLM-judge scores. The error bars ate three weeks of "trend"

We track weekly agreement between an LLM judge and human labels (Cohen's kappa) on a sample of production traces. For three weeks the point estimates told a story: 0.55, then 0.49, then 0.44. The team

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ABAman Beherabeingamanforever.hashnode.devJun 11 · 8 min read

GSoC 2026 / Week 2: Wiring the fit

Hi again. Week 2 of my GSoC with GNU Octave is done. If Week 1 was the structural shell, Week 2 is when that shell starts doing real work, the anova class can now actually fit a model and populate res

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BSBerkan Sesensesenai.hashnode.devJun 9 · 14 min read

Changepoint Detection: Finding Regime Shifts in Financial Data

Markets do not stay in one regime. The S&P 500 can cruise at 10% annualised volatility for months, then a crisis hits and volatility doubles overnight. Any model trained on the calm period is useless

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ABAman Beherabeingamanforever.hashnode.devJun 4 · 6 min read

GSoC 2026 / Week 1: The skeleton and the selector

Hi again. Week 1 of my GSoC with GNU Octave is done. This week was deliberately small in surface area and large in decisions, the kind of week where 280 lines of code take five hours of staring at exi

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Trending tags this week

AIC and BIC: Choosing the Right Model Without Overfitting

CADE — An Interesting Approach to Finding Anomalies in Multidimensional Data

What Is the Distribution of Sample Quantiles?

Stratified sampling for LLM eval sets: why your aggregate pass rate hides the regressions that matter

LDA vs PCA: Supervised Meets Unsupervised Dimensionality Reduction

Building an Autonomous Monte Carlo Engine to Predict the 2026 World Cup

We put confidence intervals on our LLM-judge scores. The error bars ate three weeks of "trend"

GSoC 2026 / Week 2: Wiring the fit

Changepoint Detection: Finding Regime Shifts in Financial Data

GSoC 2026 / Week 1: The skeleton and the selector