Machine Learning - csen2025.org

Implementing class imbalance strategies via resampling.

Resampling Fixes the Metric and Sometimes Nothing Else

I remember sitting in a windowless server room during my first industry role, staring at a training log that boasted 99.4% accuracy while the model failed to catch a single actual fraud case. It was a gut punch. We had…

Calibration of probabilities in predictive modeling.

A Confident Model Is Not Necessarily a Correct One

I remember sitting in a windowless conference room three years ago, watching a lead researcher present a model that boasted a near-perfect accuracy score, only to watch our entire deployment strategy crumble because the system was fundamentally overconfident. We had…

Auc Measures Ranking, Not Calibration

I remember sitting in a windowless lab during my postdoc, staring at a training log that claimed a near-perfect 0.99 AUC for a fraud detection model. My supervisor was ready to celebrate, but I couldn’t shake the feeling that something…

Graph illustrating the precision recall tradeoff.

Moving the Threshold Trades One Error for the Other

I remember sitting in a windowless lab during my postdoc, staring at a training curve that looked perfect on paper but was absolute garbage in practice. My supervisor was celebrating a near-perfect accuracy score, but I was watching the logs…

Evaluation metrics for classification beyond accuracy.

Accuracy Is Useless When One Class Is Rare

I remember sitting in a windowless lab during my second year of PhD work, staring at a confusion matrix that looked perfect on paper but felt fundamentally broken in practice. I had built a model that claimed 99% accuracy, yet…

t-SNE and UMAP for visualisation plot analysis

Distances in the Plot Do Not Mean What You Think

I remember sitting in a windowless lab three years ago, staring at a scatter plot that looked more like a Jackson Pollock painting than a meaningful dataset. I had spent forty-eight hours tuning hyperparameters, only to realize I was looking…

Dimensionality reduction with PCA data variance.

Keeping the Directions Where the Data Actually Varies

I remember sitting in a windowless lab three years ago, staring at a cluster of high-dimensional sensor data that looked less like a pattern and more like a static-filled television screen. I had been told that I needed a massive,…

DBSCAN and density clustering showing noise.

Clusters of Any Shape, Plus a Category for Noise

I remember sitting in a windowless lab during my PhD, staring at a visualization of a dataset that looked like a spilled bag of salt, feeling a profound sense of betrayal. Every textbook I had read promised that clustering would…

Diagram showing hierarchical clustering tree structure.

A Tree of Clusters You Cut Wherever You Like

I remember sitting in a windowless lab during my PhD, staring at a dendrogram that looked less like a meaningful data structure and more like a tangled mess of Christmas lights. I had been taught that hierarchical clustering was this…

Diagram explaining clustering with k means.

K Means Needs You to Know K Before You Start

I remember sitting in a windowless lab during my postdoc, staring at a convergence plot that refused to settle, feeling that specific, dull ache of realizing the textbook explanation was essentially a lie. We are often taught that clustering with…