Hi, regarding the Clustering chapter, I got most of it but have been puzzling over the use of the NotablePerson model to extract probablilities to feed into (I think) the KL Divergence parameters used in the FindClusters command.

It just seems to come out of nowhere, and is not much-explained in the text.

Is this something we can draw some resuable lessons from?

Thanks

To save the "unsaveable" notebook with your edits, select from the menu Format-> Options inspector and then with "Selected Notebook" selected in the drop down at top left, search for the "saveable" and set it to True.

You can try:

• Resampling your data - oversample the rare class, undersample the common class
• Use k-fold cross validation, to ensure you have balanced data in each fold
• Use a different performance metric like Recall or Precision rather than accuracy
• Cluster the common class and then use a representative sample from each cluster in the final classification to reduce the number of samples from the common class.

You can use SHAPValues to figure out the contribution of a feature. It is available as a n option for both PredictorFunction and ClassifierFunction.

 c = 
 Classify[{<|"Age" -> 32, "Height" -> 160|> -> 
    "Female", <|"Height" -> 183, "Age" -> 41|> -> 
    "Male", <|"Height" -> 123|> -> 
    "Female", <|"Height" -> 175, "Age" -> 21|> -> 
    "Male", <|"Age" -> 11|> -> 
    "Male", <|"Age" -> 52, "Height" -> 164|> -> "Female"}]

In[14]:= c[<|"Age" -> 12, "Height" -> 120|>, "SHAPValues"]
Out[14]= <|"Female" -> <|"Age" -> 0.915746, "Height" -> 26.9166|>, 
 "Male" -> <|"Age" -> 1.09201, "Height" -> 0.0371519|>|>

The way SHAP values are computed is basically to see how much results change if different features in the data are dropped.

You can use Information with "MethodParameters" option to get the parameter values for the model.

https://www.eia.gov/petroleum/weekly/

There's some data here that I can use, I have not attempted to use it. This is a challenge goal for me before I spend time wrangling the data, what ML predictions would I want to look for?

Should we use a Classify or Regression model of projected fuel prices for the next 5 years (more relevant to us because knowing this will help us prepare for the prices to either increase or fluctuate). Do we want to look further out 10 years? 20 years?

Today's lecture would suggest Regression since it's a numerical model that we are looking at. Giving us a price of fuel.

What ML methodologies wold we approach making a model trying to predict future gas prices? This data goes back up to 1980's for some of it.

Another question, are the fuel prices histrionically in line with inflation? Would we want to look for comparison data and make another ML model or make a simple assumption on the percent of inflation?

When I try to run the code in the downloaded version of the book, there is a dependency on an external tool, see screen shot. How do I resolve this?

Thanks for a quick answer. I followed the link, downloaded the .zip file. It has the same code in it.

Hi

It looks like we run in to similar issues with tomorrow's session on Clustering.

For example, the News Aggregator example returns errors, please see below,

Russell.

topics = {"Tennis", "SpaceX", "COVID-19 pandemic"};
pages = WikipediaData /@ topics;

articles = Partition[TextSentences[#], 10] & /@ articles;
articles = Map[StringRiffle, articles, {2}]; articles = 
  Flatten[Thread /@ Thread[articles -> topics]]; articletopics = Values[articles];
articles = Keys[articles];
Length[articles]

The DNA Hierarchical Clustering example isn't working right either.

I got a message (after downloading for a long time):

EntityValue::nodat: Unable to download data. Some or all results may be missing.

Some of the functions later in the example worked but Clustering Tree and Dendrogram did not :-(

How many possible classes do you expect to have? If each one-hot encoded vector is a possible class then it seems like many more classes than samples. Would it be possible to aggregate the classes into fewer "super-classes"?

Also your dataset has only two input variables right? You are definitely welcome to use a Neural Net model, but perhaps Predict would be easier to use with its automated approach to feature extraction, and access to multiple other methods.

This is awesome!

Often I have opened programming book and right off I'm in the unknown, Now it seems I can start with understanding. Then work towards the parts. There is a lot of parts within ML and statistics so the learning does not stop here. A broad overview seems to be a good start.

Machine Learning vs Statistics. What is the difference between both? That's something I learned right away. That's an way to start understanding without doing any math work myself. That's something I am trying to change my paradigm of thought. With Mathematica you do not need to do the math yourself. So, that is a different way of learning compared to step-by-step ladder. I have to be careful, as Mathematica shows with levels in many places. I can go to a level of understanding or math work. Am I understanding the same at levels? Maybe, a level can look the same to another. I think of the example of thinking of a concept or understanding, zoomed in or zoomed out. Zoom in enough and the picture does not look like anything at a different level.

ML vs Statistics what is a level above both? "Data science" I propose that you could call that a level above both. The process of getting data, then wrangling it. Turning it into something meaningful. Is it to make predictions? ML claims to be the sole purpose for. Is it to make sense of the data? Statistics is the purpose to make sense of the data with logical order or programmatically. So with those definitions you can say that "Data Science" is a level above both. As the AI is a level above everything. The models part of the AI or a level below somewhere. Understanding the where/what you want to do can help work towards a goal of where you work with either ML and statistics and so on. I have never finished a formal class of statistics myself. This definition helps me understand what statistics or ML is.

Here's a question for experienced people in statistics, Would you agree or disagree that statistics is not to make predictions?

Carl,

I like your reply! That's a fantastic point you make. The math or model can be wrong but it also can be useful and accurate.

That was what my professor said in my very first class on machine learning :)

As with any tool, there are classes of problems that benefit from machine learning and other AI tools. For example automated image recognition of manufacturing defects, or Star Trek's universal translator. (That's got to be right around the corner: Put an earbud in, and hear a person speak in your own language while cancelling the sound of their native language.)

But for classes of problems that already have robust solutions, maybe not so much. Just a quick review of some literature on error correcting codes. It's a pretty advanced field. Folks using AI generated error correcting CODECs were, at least 4 or 5 years ago, getting similar results with both techniques.

Comparing results between methods would be good at providing insight into how deep learning is actually solving problems, and may even provide insight into how organic neuro-nets actually work. Someday we may be (bio)engineering those as well.