Video

Thank you

Would be amazing if you do a video using nested data (instead of having a nominal variable, nest it and generate a model for each of the levels for example), also using the map_workflow etc.. great as always!

buenos días, sus videos son excelentes, solo pedirle para futuros videos poder enfocar mas la cámara al código ya que se ve muy pequeño, disculpas por la sugerencia.

Julia, if I'm using a topic model on CZcams comments to determine which video best explains topic modeling, how can I decide if your video or another video should be suggested? I see the model ranks comments with "gamma." If each comment is linked to a video ID, and based on gamma some or all comments rank highly in a hypothetical "topic modeling" topic, what then ? can we infer that your video is the best ?

does this work in the same way with regression problems?

Wow great video. I have nothing to do with text analysis, but I still watched the whole video

All Hail the Dino! Now quickly get it some food, or else ... Thanks for the video. It was very informative.

Thank you Julia. Can this work if my version of 'type' has 5-8 categories? Where the final output is More like 'X' where 'X' is each category label? Is there a way to get more words in each prediction fold? So in the final output it could look like 3 words for each more like? Thank you, again.

You are the queen of data analysis. Thanks for the video!

Hi @JuliaSilge - thanks for your wonderful and helpful videos. I am trying to replicate your code with my own dataset and I am getting the following error when trying to initiate the tuning of the model: > xgb_rs <- + tune_race_anova( + object = xgb_wf, + resamples = dens_folds, + grid = 15, + control = control_race(verbose_elim = TRUE) + ) ℹ Evaluating against the initial 3 burn-in resamples. i Creating pre-processing data to finalize unknown parameter: mtry Error in `tune::tune_grid()`: ! Package install is required for xgboost. Run `rlang::last_trace()` to see where the error occurred. It says that a package install is required. Any idea about what package may be missing? I installed the 'tune' package and still gives me the same error. Any thoughts are appreciated. Thanks, Kevin

Nice explanation. You could have used the searchK() function to show us how to select the range for K.

What appearance theme are you using here?

Thanks Julia, love your videos! To get a more accurate sense of performance, would it be helpful to use {spatialsample} to account for spatial autocorrelation?

Hey Julia, great tutorial. I had a question. Here you used Subject_Matter as the only high cardinality variable. If we have a dataset where there are multiple columns with high cardinality, can the recipe method be used in such a case for all the high cardinality columns ?

I always love your videos Julia! I learn so much every time. Thank you for all the screencasts! Hopefully you haven't stopped and I am looking forward for more!

With a left join you keep all the rows

thanks for this video Julia <3

Hi, I have these data with 35 variables and want to run some regression(RF,xgboost, etc..) on it. I am new to R and want to know if you have any special online training that I can register for?

could you make a video explaining how to deploy a model with shiny

good explanation

Thank you Julia for another excellent video. I have been trying to replicate your methods of generating a Canadian Provincial Map with the outlines of the provinces. For some reason map_data doesn't include borders to provinces the way that the states is set up. Do you have any ideas, how I can use map_data to do this? I am using map_data("world", "Canada") to get the provinces.

This channel is pure gold.

Bitters are good for health!

After recently having hacked my way through a similar attempt to visualize data by state, this was a like receiving "the answer sheet" from your teacher. Really appreciate the smart use of usmap information, building the tibble to facilitate the join of state abbr to state(lower) and the ever-useful window commands in dplyr!

(I'm very very new to all this) Getting the prior and the empirical/posterior from the same data seems counterintuitive to me and a bit like confirmation bias.

Thanks, another investing video.

We see Julia we click Like !! :)

brilliant work, thank you Julia!

I've been watching your text mining videos recently. I'm about to start a project mining case notes for info on clients' relatives. I'm hoping to build a model that can predict if a case note contains that info or not. Any tips would be appreciated!

Yes, new video. its been a while. love this content.

Excellent, thank you.

As Usual, Brilliant work from a brilliant data scientist...

0:08

Your videos are therapeutic haha

Clear explained and direct to the point! Thank you Julia.

Thanks for the tip on using type.predict = "response" in the broom::augment function. I learn something new from each of your vides, so thanks for the work that you put into making these.

YES! This is evidence supporting my hypothesis that Jodie Whittaker is a great actress, but Chris Chibnall is a terrible writer 😅. It isn't conclusive obviously. We would need observations from when they worked separately to get better proof.

please avoid background colour black. use eyesight friendly colour

Thank you for the great video. I have one question, assuming the best model was one of the tuned random forest models, how would we extract the parsnip object to see the tuned hyperparameters i.e mtry and min_n?

Hey Julia. Writing you here although its not related to this specific video. I am using the tidylo::bind log odds for a project but the fact that the order of set/feature is different than the one in tidytext::bind tf idf (which requires term and then document) makes it hard to easily encode it for shiny (with f <- type_of_algorithm; f(docs/sets, terms/fts, n=n), for example). Any chance you change the tidylo order? Obviously it can be done with a simple if(){} else{} but its much cleaner to use the `f <-select_algo` approach :)

The extra brackets of copilot can be really annoying! When I turn it on I've started to use enter for RStudio's autocomplete (tab just takes copilot's often incorrect suggestion), does anyone have a better solution?

Amazing analysis! :O

Great episode like always Just a little whim from my side: would you mind doing videos about TDA real-life application ? And how can implement Spark on models that aren't on Parnsip ? Thank you 😅

Hey Julia, thanks for you video ! I really like these.. I had one question about the std_var per writer... why did you divided it by the number of observations ?

Interesting! Could you do more practical applications of Bayesian statistics. I see it more and more asked and required by bigger tech companies.

So fun! Thanks for the video

Big fan of Dr Who too

adding this into my EDA tool box

Sometimes copilot feels more like a burden than a companion. Thanks Julia, I always learn a ton with these videos.

Amazing work! you're so inspiring, thanks for sharing!