Explanation for Kaggle mission - why do we loop through k-values 49 times?

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#1

Hi,

I’m hoping someone can help me with some explanation. I’ve been going through the Kaggle fundamentals missions and they’ve all been very interesting and well explained, however, there is now something I can’t understand.

In the Model Selection And Tuning mission, step 4 (Exploring Different K Values) we are told to use the Python range class to loop through odd values of K for the Titanic data set and see which one is more accurate. My problem is, why do we loop through 49 times? Is this an arbitrary number and if I had a completely different set, would it make sense I run through that 49 times? Is it specific to this data set because e.g. it has 49 features?

I did run shape to see how many columns all_X has in this instance and it says 37 so not sure where 49 is coming from!

Thanks for any help

#2

K represents the number of neighbors used in the K-nearest neighbors algorithm.
49 looks random here, just to give you a range of results. It also doesn’t make sense to have K >= total number of rows in the data since you can’t have more neighbors than you have data.
For the effect of number of columns on K selection, my intuition is more columns makes data more sparsely separated and thus having a larger K allows less overfitting to one neighbor.
This link demonstrates overfitting in KNN, but also more importantly bias-variance tradeoff: http://scott.fortmann-roe.com/docs/BiasVariance.html

1 Like
#3

Thank you hanqi - that makes sense :slight_smile: the article is very interesting!