# \#tuning

**URL:** https://discourse.julialang.org/tag/tuning/327.md

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## [Learning curve for validation dataset](https://discourse.julialang.org/t/learning-curve-for-validation-dataset/118529)

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**Author:** [@niltsz](https://discourse.julialang.org/u/niltsz)\
**Replies:** 1\
**Last updated:** [August 26, 2024, 8:17am UTC](https://discourse.julialang.org/t/learning-curve-for-validation-dataset/118529 "2024-08-26T08:17:31Z")

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I want to train a ML model with MLJ, for example an XGBoost Regressor with L2 Loss. I split my dataset into three parts, train/val/test, and during training I want to train on the train dataset but also plot the loss/ac…

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## [Automatic Creation of a Grid of Tuning Parameters](https://discourse.julialang.org/t/automatic-creation-of-a-grid-of-tuning-parameters/71902)

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**Author:** [@Rahul](https://discourse.julialang.org/u/Rahul)\
**Replies:** 4\
**Last updated:** [November 23, 2021, 9:10pm UTC](https://discourse.julialang.org/t/automatic-creation-of-a-grid-of-tuning-parameters/71902 "2021-11-23T21:10:17Z")

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Currently, in R’s caret package if we do something like this: fitControl \<- trainControl(method = "repeatedcv", number=10,repeats=5) model\_rf = train(responder~ ., data=trainData, method='rf',trControl=fitControl) a gr…

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## [Best ways to do hyper-parameter tuning](https://discourse.julialang.org/t/best-ways-to-do-hyper-parameter-tuning/33010)

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**Author:** [@Albert\_Zevelev](https://discourse.julialang.org/u/Albert_Zevelev)\
**Replies:** 3\
**Last updated:** [January 6, 2020, 2:28am UTC](https://discourse.julialang.org/t/best-ways-to-do-hyper-parameter-tuning/33010 "2020-01-06T02:28:30Z")

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I’d like to tune a model in JLBoost (an awesome, all Julia package by @xiaodai builds on XGBoost, LightGBM, & Catboost). using RDatasets, DataFrames, JLBoost, MLJ; d = dataset("MASS", "Boston"); train, test = partition…