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Recent items include:

  • Can Conformal Prediction Take You from Accurate to Reliable Machine Learning?
  • Choosing the size of the calibration set in conformal prediction with ahead::conformalize()
  • Confidence Procedures Are Production Code

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towardsdatascience.com /3 days ago

Can Conformal Prediction Take You from Accurate to Reliable Machine Learning?

ML model adoption doesn't stop with acceptance of accuracy; it starts there. The post Can Conformal Prediction Take You from Accurate to Reliable Machine Learning? appeared first o...

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r-bloggers.com /5 days ago

Choosing the size of the calibration set in conformal prediction with ahead::conformalize()

This post explores the impact of the split_prob parameter in the ahead::conformalize() function on the resulting prediction intervals. Continue reading: Choosing the size of the...

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dev.to /1 month ago

Confidence Procedures Are Production Code

A clean sample can sound stronger than it is. Someone checks a handful of items, finds no exceptions, and the sentence wants to become broader than the evidence: the process worked...

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r-bloggers.com /1 month ago

Model-agnostic prediction intervals in Python and R: does nnetsauce’s `QuantileRegressor` hold up?

Point predictions tell you what a model *thinks* will happen. They don't tell you how much to trust that number. nnetsauce's `QuantileRegressor` takes any sklearn-compatible regres...

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bioengineer.org /1 week ago

New FLARE Framework Brings Statistical Rigor to Nonlinear Clinical Risk Prediction

A new framework called FLARE combines nonlinear machine learning flexibility with closed-form uncertainty estimates, statistical tests, and patient-level interaction analysis for c...

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bioengineer.org

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dev.to

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towardsdatascience.com

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r-bloggers.com

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