Discover and read the best of Twitter Threads about #SLICED
Most recents (3)
NEW BLOG POST: Machine learning in a hurry: what I've learned from the #SLICED ML competition
varianceexplained.org/r/sliced-ml/ #rstats
varianceexplained.org/r/sliced-ml/ #rstats
You can vote for me to go to the playoffs of #SLICED here! forms.gle/1JNC31EAbxTAaf…
To summarize what I've learned so far from #SLICED 🧵
To summarize what I've learned so far from #SLICED 🧵
1. The tidymodels #rstats package is a really powerful "pit of success" for machine learning 🔥tidymodels.org
Perhaps the most important innovation is the combination of recipes (feature engineering) and models into an encapsulated workflow. (Continued)
Perhaps the most important innovation is the combination of recipes (feature engineering) and models into an encapsulated workflow. (Continued)
SINCE @kierisi has threatened to sarcastically/chaotically say incorrect things about p-values during #sliced tonight 😱 just to annoy people 😉,
I thought I’d do a quick thread on what a 🚨p-value🚨actually is.
🧵
(1/n)
I thought I’d do a quick thread on what a 🚨p-value🚨actually is.
🧵
(1/n)
Computationally, a p-value is p(data as extreme as ours | null). Imagine a 🌎 where the null hypothesis is true (e.g. there is no difference in cat fur shininess for cats eating food A vs. food B for 2 weeks), and see how extreme your observed data would be in that 🌎 (2/n)
So, you can think of the p-value as representing our data’s compatibility with the null hypothesis. Low p-values mean our data is not very likely in a world where the null is true. High p-values mean it is relatively likely. (3/n)
Still responding to folks re: my transition to data science post! I'll get to everyone, promise!
Given the interest I thought people might want to know the (almost all free/low cost!) resources I used to train myself for a data science role
A (hopefully helpful) 🧵
Given the interest I thought people might want to know the (almost all free/low cost!) resources I used to train myself for a data science role
A (hopefully helpful) 🧵
R, Part I
My first real #rstats learning experience was using swirl. I loved that I could use it inside of R (rather than having to go back and forth between the resource and the RStudio console)
swirlstats.com/students.html
My first real #rstats learning experience was using swirl. I loved that I could use it inside of R (rather than having to go back and forth between the resource and the RStudio console)
swirlstats.com/students.html
R, Part II
A cliche rec, but it's cliche for a reason. R for Data Science by @hadleywickham & @StatGarrett transitioned me from "kind of messing around" to "wow, I did that cool thing" in R. It's absolutely a steal that it's available for free
r4ds.had.co.nz
A cliche rec, but it's cliche for a reason. R for Data Science by @hadleywickham & @StatGarrett transitioned me from "kind of messing around" to "wow, I did that cool thing" in R. It's absolutely a steal that it's available for free
r4ds.had.co.nz
