Using Unsupervised Learning for Early Data Drift Detection
When labels arrive late, production teams need early signals that the input environment has changed. Unsupervised drift detection can provide those signals, ...
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Distance Concentration: Why Nearest Neighbours Stop Meaning Anything in High Dimensions
A monitoring system computes a two-thousand-feature signature per machine and flags any machine whose nearest neighbours are far away. In two thousand dimens...
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Censored Labels in Supervised Learning: When 'No Event Yet' Is Not a Negative
A churn model trained on a database extract learns that customers who joined last month never churn. It reports an AUC of 0.90, predicts under one percent ri...
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Learning Curves: Deciding Whether More Data Will Help
The request arrives as a budget line: ten thousand more labels, at two euros each. Whether they are worth it is not a matter of opinion. The learning curve s...
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Active Learning for Machine Learning: Getting More Value from Fewer Labels
Active learning improves machine learning by choosing which examples to label, not merely by asking for more labeled data.
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Regression to the Mean: The Improvement You Did Not Cause
Pick the worst ten machines, sites or agents, intervene, and watch them improve. Most of that improvement was going to happen anyway, and there is a formula ...
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Offline Change-Point Detection: Segmenting a Series After the Fact
Sequential detection asks whether something has changed as of now. The retrospective question is different: given two years of a sensor's history, where did ...
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Selective Prediction in Machine Learning: When Models Should Abstain
Selective prediction gives machine learning systems a third option: predict when confidence is adequate and abstain when the cost of being wrong is too high.
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Synthetic Control: Evaluating an Intervention on One Unit
One plant got the new maintenance regime. Before-after says it did nothing, because demand was rising. Comparing against the other plants says it did too lit...
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Representation Learning for Tabular Data: Beyond Manual Feature Engineering
Representation learning for tabular data is not about replacing feature engineering blindly. It is about learning useful structure while respecting the const...
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