Thanks for the tutorials. I believe those who want to establish a solid base on statistics and ML, would be happy to see your tutorials on ML data pipeline (from creating a project, use virtual environment for package management, develop ML and deploy the model using Shiny, bslib libraries). The model will not only generate predictions but will also create some key metrics in graphical format, like AUC_ROC curve, precision recall curve or feature importance.
I know it's too much but this type of workflow is indispensable now-a-days.
Hi,
Thanks for the tutorials. I believe those who want to establish a solid base on statistics and ML, would be happy to see your tutorials on ML data pipeline (from creating a project, use virtual environment for package management, develop ML and deploy the model using Shiny, bslib libraries). The model will not only generate predictions but will also create some key metrics in graphical format, like AUC_ROC curve, precision recall curve or feature importance. I know it's too much but this type of workflow is indispensable now-a-days.
Thanks