# \#standardized

**URL:** https://discourse.julialang.org/tag/standardized/707.md

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## [MLJ/MljFlux standardisation of variables in each cross-validation fold](https://discourse.julialang.org/t/mlj-mljflux-standardisation-of-variables-in-each-cross-validation-fold/136093)

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**Author:** [@Pablo\_G-D](https://discourse.julialang.org/u/Pablo_G-D)\
**Replies:** 4\
**Last updated:** [March 10, 2026, 3:54pm UTC](https://discourse.julialang.org/t/mlj-mljflux-standardisation-of-variables-in-each-cross-validation-fold/136093 "2026-03-10T15:54:57Z")

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Hi all, I am learning Julia, and so far I have implemented a few Bayesian models using Turing and other packages. I found it quite fast and reliable. I am now building a neural network model for a land-use/land-cover t…

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## [\[ANN\] CircuitComponentRounding.jl](https://discourse.julialang.org/t/ann-circuitcomponentrounding-jl/62496)

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**Author:** [@TheLateKronos](https://discourse.julialang.org/u/TheLateKronos)\
**Replies:** 1\
**Last updated:** [May 17, 2023, 8:42am UTC](https://discourse.julialang.org/t/ann-circuitcomponentrounding-jl/62496 "2023-05-17T08:42:35Z")

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I am pleased to announce CircuitComponentRounding.jl, a Julia package for rounding a set of e.g. calculated values to their nearest standardized values, which will actually exist in normal component storages! The followi…

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## [Right way of applying \`inverse\_transform\`](https://discourse.julialang.org/t/right-way-of-applying-inverse-transform/83124)

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**Author:** [@ctrebbau](https://discourse.julialang.org/u/ctrebbau)\
**Replies:** 3\
**Last updated:** [June 25, 2022, 6:27pm UTC](https://discourse.julialang.org/t/right-way-of-applying-inverse-transform/83124 "2022-06-25T18:27:18Z")

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Hi, I have a setup like this: dftrain, dftest = partition(df, 0.7, shuffle=true, rng=123) datapipe = ContinuousEncoder() |\> Standardizer() datatrans\_mach = machine(datapipe, dftrain) |\> fit! normalized\_train = MLJ.tran…