Row 6352

Row ID: 6352 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 6352 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

Maybe a stupid trivia question, but I can't figure it out. ML calls features features, stats calls features predictors, math calls features variables, engineering calls features variables too.

I know what they are, but WHY do we call them features? Does anyone know the origin story?

**EDIT:** You all gave me some good leads; I think I have found a plausible answer: It likely comes from cognitive psychology. The [paper introducing the perceptron](https://psycnet.apa.org/record/1959-09865-001) (arguably the first step towards neural networks) refers to inputs as stimuli, but also notes that encoding stimuli into a small set of robust features helps performance:

>As the number of responses in the system increases, the performance becomes progressively poorer, if every response is made mutually exclusive of all alternatives. One method of avoiding this deterioration (described in detail in Rosenblatt, 15) is through the binary coding of responses. In this case, i**nstead of representing 100 different stimulus patterns by 100 distinct, mutually exclusive responses, a limited number of discriminating features is found, each of which can be independently recognized as being present or absent**, and consequently can be represented by a single pair of mutually exclusive responses.

(highlighting is mine)

And later it concludes

>The performance of the system can be improved by the use of a contour-sensitive projection area, and by the use of a binary response system, in which **each response, or "bit," corresponds to some independent feature or attribute of the stimulus**.

(highlighting mine)

Field	Value
text	Maybe a stupid trivia question, but I can't figure it out. ML calls features features, stats calls features predictors, math calls features variables, engineering calls features variables too. I know what they are, but WHY do we call them features? Does anyone know the origin story? EDIT: You all gave me some good leads; I think I have found a plausible answer: It likely comes from cognitive psychology. The [paper introducing the perceptron](https://psycnet.apa.org/record/1959-09865-001) (…
label	r/machinelearning
dataType	post
communityName	r/MachineLearning
datetime	2024-05-09
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Raw Record

{
  "text": "Maybe a stupid trivia question, but I can't figure it out. ML calls features features, stats calls features predictors, math calls features variables, engineering calls features variables too.\n\nI know what they are, but WHY do we call them features? Does anyone know the origin story?\n\n**EDIT:** You all gave me some good leads; I think I have found a plausible answer: It likely comes from cognitive psychology. The [paper introducing the perceptron](https://psycnet.apa.org/record/1959-09865-001) (arguably the first step towards neural networks) refers to inputs as stimuli, but also notes that encoding stimuli into a small set of robust features helps performance:\n\n>As the number of responses in the system increases, the performance becomes progressively poorer, if every response is made mutually exclusive of all alternatives. One method of avoiding this deterioration (described in detail in Rosenblatt, 15) is through the binary coding of responses. In this case, i**nstead of representing 100 different stimulus patterns by 100 distinct, mutually exclusive responses, a limited number of discriminating features is found, each of which can be independently recognized as being present or absent**, and consequently can be represented by a single pair of mutually exclusive responses.\n\n(highlighting is mine)\n\nAnd later it concludes\n\n>The performance of the system can be improved by the use of a contour-sensitive projection area, and by the use of a binary response system, in which **each response, or \"bit,\" corresponds to some independent feature or attribute of the stimulus**.\n\n(highlighting mine)",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-09",
  "username_encoded": "Z0FBQUFBQm5LakwyaWFXdl91WXlLX0RUdjZpOW9mOEZMbllhalJTbDE0WkIyVWxOXzlyeW9POXVocmRMSmhOWXU1SVdJdUFlMWNZdlhwM2ZjS2dYWXZBNEFTMnF4ck1rQmc9PQ==",
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}

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Entry Information

Entry ID: 6352
Repository: Axioma AXP
Dataset: arrmlet/reddit_dataset_36
Total Entries: 100,000