Gordon Forbes
@gforb.bsky.social
I love Netflix for their data science blog and The BBC for their ggplot2 resources.
On tabular health data, time and time again, I see linear (or generalised linear models) perform as well or better than machine learning algorithms that avoid linearity assumptions.
I am surprised by this, as the linearity assumption is unlikely to be true.
Does anyone else see this? Why is this?
I am surprised by this, as the linearity assumption is unlikely to be true.
Does anyone else see this? Why is this?
May 22, 2025 at 9:10 AM
On tabular health data, time and time again, I see linear (or generalised linear models) perform as well or better than machine learning algorithms that avoid linearity assumptions.
I am surprised by this, as the linearity assumption is unlikely to be true.
Does anyone else see this? Why is this?
I am surprised by this, as the linearity assumption is unlikely to be true.
Does anyone else see this? Why is this?
On the reproducibility crisis coming for epidemiology from 2008. I think still true today although probably a lot more good examples.
Pre-specified primary endpoints + analysis plans, and corrections for multiple testing are not routinely used in observational studies.
t.co/HPKYqhP1de
Pre-specified primary endpoints + analysis plans, and corrections for multiple testing are not routinely used in observational studies.
t.co/HPKYqhP1de
December 18, 2024 at 4:09 PM
On the reproducibility crisis coming for epidemiology from 2008. I think still true today although probably a lot more good examples.
Pre-specified primary endpoints + analysis plans, and corrections for multiple testing are not routinely used in observational studies.
t.co/HPKYqhP1de
Pre-specified primary endpoints + analysis plans, and corrections for multiple testing are not routinely used in observational studies.
t.co/HPKYqhP1de
I would love to see that post. I've used the end argument to get better results. The plot on the right is from using
+ scale_colour_viridis_d(end = 0.8)
+ scale_colour_viridis_d(end = 0.8)
December 18, 2024 at 12:39 PM
I would love to see that post. I've used the end argument to get better results. The plot on the right is from using
+ scale_colour_viridis_d(end = 0.8)
+ scale_colour_viridis_d(end = 0.8)
December 18, 2024 at 12:23 PM
