Discover and read the best of Twitter Threads about #SLICED

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NEW BLOG POST: Machine learning in a hurry: what I've learned from the #SLICED ML competition #rstats Meme with text:  Planes labeled with "LINEAR MODELS WIT
You can vote for me to go to the playoffs of #SLICED here!…

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 🔥

Perhaps the most important innovation is the combination of recipes (feature engineering) and models into an encapsulated workflow. (Continued)
Read 11 tweets
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.

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)
Read 20 tweets
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) 🧵
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)
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
Read 14 tweets

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