Row 97296

Row ID: 97296 | Dataset Entry | Axioma AXP Content Repository

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Context agnostic measures in unsupervised learning should be taken with a grain of salt. Imagine I cluster a set of people the following ways:

- split by gender - split by age - split by favourite hobby

If I'm using the clusters to sell insurance maybe I just need the second solution. If I'm exploring the data to understand patterns, maybe I want to try clustering on all three simultaneously.

Silhouette scores are good for checking that your clustering algorithm isn't absolutely daft at finding clusters in the data. If you have just one variable, height, and your sample is NBA vs. Kids in grade 1, a cluster solution that doesn't split up NBA vs. Grade 1 will have a worse silhouette than a cluster solution that does. But once you have an algorithm that can reasonably do that kind of separation, the nitty gritty details of which solution has the best silhouette in a high dimensional setting is essentially meaningless from a practical point of view and more of a toy flex like writing a complex one liner of code. There's no 1:1 connection between silhouette (or any) purely data driven cluster quality metric and real life usefulness because usefulness depends on your context.

Why are you clustering the data to begin with?

FieldValue
text Context agnostic measures in unsupervised learning should be taken with a grain of salt. Imagine I cluster a set of people the following ways: - split by gender - split by age - split by favourite hobby If I'm using the clusters to sell insurance maybe I just need the second solution. If I'm exploring the data to understand patterns, maybe I want to try clustering on all three simultaneously. Silhouette scores are good for checking that your clustering algorithm isn't absolutely daft at fin…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-25
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url_encoded Z0FBQUFBQm5LalBCZlJvODBXWi1QTExKRXFITW5QSWQxZW9DQTZqbFNraGtCdV9VNzVJWG9sd0lFQ2dLa3g3elhWa2xyeGRQYm13c3pFOEY4akUxcVkyeEgwb2Z3aUNrRkM3U2hmR0VmTlp4cVl5WmY0UnFlT2hrVnNoUVI1T1o1MG9wWk9PcWlXcmYyWWVEaFVpZzVDTlNXN3VTSlpQLUxpNGZCeVJJZXloa1NTUTFkeXF1eGlPeXVNNnVEN3I1bU9uNm16cEVCTGpwRXJ2TDJ3RlYyV0JaTlBkczc1U2Jkdz09

Raw Record

{
  "text": "Context agnostic measures in unsupervised learning should be taken with a grain of salt. Imagine I cluster a set of people the following ways: \n\n- split by gender\n- split by age\n- split by favourite hobby\n\nIf I'm using the clusters to sell insurance maybe I just need the second solution. If I'm exploring the data to understand patterns, maybe I want to try clustering on all three simultaneously. \n\nSilhouette scores are good for checking that your clustering algorithm isn't absolutely daft at finding clusters in the data. If you have just one variable, height, and your sample is NBA vs. Kids in grade 1, a cluster solution that doesn't split up NBA vs. Grade 1 will have a worse silhouette than a cluster solution that does. But once you have an algorithm that can reasonably do that kind of separation, the nitty gritty details of which solution has the best silhouette in a high dimensional setting is essentially meaningless from a practical point of view and more of a toy flex like writing a complex one liner of code. There's no 1:1 connection between silhouette (or any) purely data driven cluster quality metric and real life usefulness because usefulness depends on your context.\n\nWhy are you clustering the data to begin with?",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-25",
  "username_encoded": "Z0FBQUFBQm5Lak12T3VqVWNGQU1GSmcxWTg1VkFrVU0wSFV1RnBxZHdxcTNoQXhaLVFkQktpcjNsWDZNQW5ra2xFTkcyVXVoNzBxa0lNRGVSMXBtYzVxN3B0Q1NsVXFacnc9PQ==",
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}
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