smu | simple markup - markdown like syntax

I am using the R programming language. I created some data and make a KNN graph of this data. Then I performed clustering on this graph. Now, I want to superimpose the clusters on top of the graph.

Here is an example I made up (source: https://michael.hahsler.net/SMU/EMIS8331/material/jpclust.html) - suppose we have a dataset with 3 variables : the longitude of the house, the latitude of the house and the price of the house (we "scale" all these variables since the "price" and the "long/lat" are in different units). We can then make a KNN graph (using R software):

I'm writing a function with 2 params (data, keys) that will take schools_list as the first param (see below) and a tuple groupby_keys as the second param:

I have been attempting to copy a named range in one excel sheet to a named range in another worksheet

I am using the R programming language. I incorporated my own code along with a lengthy tutorial over here : https://michael.hahsler.net/SMU/EMIS7332/R/viz_classifier.html . In the end, I produced a visual "plot" (see the end of this code, "final_plot")

I am using the R programming language and following this tutorial over here: https://michael.hahsler.net/SMU/EMIS7332/R/viz_classifier.html .

I simulated some data and plotted the results as per the tutorial:

I am trying to add range bars to nodes as described in the treedata book. Here is an example from the book of what I am trying to achieve:

Note the red bars.

Here is code that should create a tree image with red bars:

The picture above is what I'm trying to replicate. I just don't know if I'm going about it the right way. I'm working with the FakeNewsChallenge dataset and its extremely unbalanced, and I'm trying to replicate and improve on a method used in a paper.

Agree - 7.36%

Disagree - 1.68%

Discuss - 17.82%

Unrelated - 73.13%

I'm splitting the data in this way:

(split dataset into 67/33 split)

• train 67%, test 33%

(split training further 80/20 for validation)

• training 80%, validation 20%

(Then split training and validation using 3 fold cross validation set)

As an aside, getting that 1.68% of disagree and agree has been extremely difficult.

This is where I'm having an issue as it's not making total sense to me. Is the validation set created in the 80/20 split being stratified as well in the 5fold?

Here is where I am at currently:

Split data into 67% Training Set and 33% Test Set

I want to get a responsive layout, in my code bellow

the code: