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Logistic regression explained: predicting probability.
  • Dr. Ingrid Falk-Weller
  • April 3, 2026
  • (0)
  • Machine Learning

Predicting Probability, Not Category

I remember sitting in a windowless graduate lab five years ago, staring at a textbook that tried to explain logistic regression through a dense thicket of calculus and abstract probability distributions. It felt like the author was trying to hide…

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Four important linear regression assumptions.
  • Dr. Ingrid Falk-Weller
  • March 24, 2026
  • (0)
  • Machine Learning

Four Assumptions Nobody Checks and All of Them Matter

I remember sitting in a windowless lab during my PhD years, staring at a beautiful, high-performing model that I was certain was a breakthrough, only to realize later that I had ignored the most basic linear regression assumptions in favor…

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Understanding feature scaling and why it matters.
  • Dr. Ingrid Falk-Weller
  • March 20, 2026
  • (0)
  • Machine Learning

Gradient Descent Struggles When Features Differ by Orders of Magnitude

I remember sitting in a windowless lab at my old university, staring at a loss curve that looked less like a smooth descent and more like a jagged mountain range during a tectonic shift. I had spent three days tuning…

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Comparison of categorical encoding methods.
  • Dr. Ingrid Falk-Weller
  • March 9, 2026
  • (0)
  • Machine Learning

High Cardinality Breaks One Hot Encoding Quietly

I spent three months in a research lab during my PhD chasing a marginal accuracy gain, only to realize I had spent half that time debugging a catastrophic data leak caused by a poorly implemented one-hot encoder. It is infuriating…

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Analyzing the impact of handling missing data.
  • Dr. Ingrid Falk-Weller
  • March 7, 2026
  • (0)
  • Machine Learning

Why the Data Is Missing Matters More Than That It Is

I remember sitting in a windowless lab during my PhD, staring at a dataset that looked less like a coherent signal and more like a sieve. I had spent three weeks trying to implement a sophisticated, multi-stage Bayesian imputation model…

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Learning feature engineering fundamentals for model training.
  • Dr. Ingrid Falk-Weller
  • February 28, 2026
  • (0)
  • Machine Learning

The Model Learns What You Show It, Nothing More

I remember sitting in a windowless lab during my postdoc, staring at a loss curve that refused to budge, no matter how many layers of transformer blocks I stacked onto the architecture. I had spent weeks optimizing hyperparameters and chasing…

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Diagram illustrating the bias variance tradeoff.
  • Dr. Ingrid Falk-Weller
  • February 22, 2026
  • (0)
  • Machine Learning

Underfitting and Overfitting Are the Same Dial

I remember sitting in a windowless lab three years ago, staring at a model that had achieved near-perfect accuracy on my training set, only to watch it fall apart the moment I fed it real-world data. I had fallen into…

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Cross validation explained through multiple estimates.
  • Dr. Ingrid Falk-Weller
  • February 10, 2026
  • (0)
  • Machine Learning

Five Estimates Beat One Lucky Split

I remember sitting in a windowless lab during my PhD, staring at a training curve that looked absolutely perfect—too perfect. I had spent weeks tuning a model, feeling that intoxicating rush of seeing near-zero error rates, only to have the…

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Applying regularisation techniques to improve model generalisation.
  • Dr. Ingrid Falk-Weller
  • February 5, 2026
  • (0)
  • Machine Learning

Making the Model Worse on Purpose So It Generalises

I spent three years in academia watching researchers treat regularization techniques like some kind of dark magic—a collection of opaque hyperparameters you just “tune” until the loss curve looks pretty. I remember sitting in a windowless lab, staring at a…

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Visualizing overfitting and how to see it.
  • Dr. Ingrid Falk-Weller
  • February 1, 2026
  • (0)
  • Machine Learning

A Model That Memorises Looks Perfect Until It Meets Reality

I remember sitting in a windowless lab three years ago, staring at a loss curve that looked like a work of art—a perfect, smooth descent toward zero. My training metrics were flawless, and I felt that rush of dopamine we…

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