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Hi @Curious
it is correct, you can take a look at Auto Model with model type "Deep Learning" to have an example. It is always better to do cross validation, and maybe reduce the folds if it takes too long.
It is good to remark that cross validation is used only for measuring the model performance with a reasonable bias. Depending of whether you need to optimize hyperparameters or not, cross validation may not be needed in a production environment.
Regards,
Sebastian
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Hi @Curious
You can use cross-validation for deep learning as the results are consistent compared to split validation. Personally, I use cross-validation for every model I develop as this gives confidence about the results.
Thanks
You can use cross-validation for deep learning as the results are consistent compared to split validation. Personally, I use cross-validation for every model I develop as this gives confidence about the results.
Thanks
Hi @Curious
it is correct, you can take a look at Auto Model with model type "Deep Learning" to have an example. It is always better to do cross validation, and maybe reduce the folds if it takes too long.
It is good to remark that cross validation is used only for measuring the model performance with a reasonable bias. Depending of whether you need to optimize hyperparameters or not, cross validation may not be needed in a production environment.
Regards,
Sebastian
You can use cross-validation for deep learning as the results are consistent compared to split validation. Personally, I use cross-validation for every model I develop as this gives confidence about the results.
Thanks