What an ID column, a date string, and a missing value taught me. The post My Machine Learning Pipeline Cheated, and Cross-Validation Never Noticed appeared first on Towards Data Science .
The article titled "What We Miss About Missing Values" explores the often overlooked assumptions embedded in the data we observe, particularly focusing on how missing values can influence analysis and interpretation. It delves into the hidden biases and methodological implications that arise when data is incomplete, urging readers to consider these factors when working with real-world datasets.
Enterprise Document Intelligence [Vol. 1 #8C] - Structured output is the start of validation, not the end: check the evidence, accept not-found, loop the feedback The post Validating the RAG Answer Before the User Sees It: Spans, Quotes, and the Feedback Loop appeared first on Towards Data Science .
A preprocessing pipeline let my car price model peek at the test set before the exam, and the twelve points of R squared it cheated its way to The post My Model Was Cheating on Its Own Test appeared first on Towards Data Science .
Enterprise Document Intelligence [Vol. 1 #3] - Why the ML toolkit (hyperparameter sweeps, train/test splits, explainability frameworks) solves the wrong problem, and what to use instead The post RAG Is Not Machine Learning, and the ML Toolkit Solves the Wrong Problem appeared first on Towards Data Science .
How a single evaluation choice inflated my results by 25 points, and what rebuilding honestly taught me about ML systems people might depend on The post My Fall-Detection Model Scored 94%, and It Was Lying to Me appeared first on Towards Data Science .
The article discusses how the reliability mechanisms added to large language model (LLM) pipelines can lead to confident but incorrect outputs, especially when the correct answer is absent. It examines the behavior of pipelines in such scenarios and highlights the paradox where safeguards intended to improve accuracy may actually reinforce errors. The piece underscores the importance of understanding pipeline responses when faced with missing or ambiguous information.
The article describes an experiment where an AI agent was able to identify incorrect model decisions while interacting with a web page, yet failed to correct the errors. By replaying correct decisions through four intentionally flawed browser actions, the author demonstrates that a simple page check missed half of the erroneous clicks. This illustrates that detecting a fault and successfully recovering from it are distinct capabilities.
The article describes a claim‑checker tool built for writing with Codex that successfully identified the correct passage but failed to detect a mismatch, illustrating the limitations of rule‑based checks. The audit highlights where these automated checks break down and provides insights into improving claim verification processes.
The analytics career I signed up for five years ago doesn't exist anymore, and honestly, I am fine with that. The post How I’m Making Sure My Analytics Career Doesn’t Get Eaten by AI appeared first on Towards Data Science .
The article discusses how embeddings can misinterpret subtle differences, such as confusing ZX-4821 with ZX-4812, and highlights a 1994 algorithm that is missing from many Retrieval-Augmented Generation (RAG) pipelines.
The article describes how to deploy a trained churn classifier as a FastAPI service so that other software can call it. It focuses on the practical steps needed to transform a model that performs well in isolation into a usable, callable API. The post is aimed at readers who want to make their machine‑learning models accessible in real-world applications.
The downside of conference travel The post Last Month’s Machine Learning Lessons Learned appeared first on Towards Data Science .