Sharp Turn in Logistic Regression

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Discussion Overview

The discussion revolves around the characteristics of logistic regression, specifically focusing on the behavior of the logistic curve when all data points beyond a certain threshold are either all successes or all failures. Participants explore the implications of this scenario on the logistic function's shape.

Discussion Character

  • Exploratory, Technical explanation

Main Points Raised

  • One participant describes a situation in logistic regression where all data points beyond a fixed point are either all successes or all failures, leading to a sharp turn in the logistic curve.
  • Another participant suggests that this behavior may relate to the sigmoid function or the ReLu function.
  • A later reply confirms the relevance of the sigmoid function to the discussion.
  • There is a question about whether the term "saturation" applies to this scenario, with a participant expressing uncertainty about the terminology.

Areas of Agreement / Disagreement

The discussion does not reach a consensus on the terminology or the implications of the sharp turn in the logistic regression context, as participants express uncertainty and explore different terms.

Contextual Notes

Participants do not clarify the specific definitions or implications of terms like "saturation" in relation to logistic regression, leaving some assumptions and definitions unresolved.

WWGD
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Hi
I am trying to remember the name of the situation in logistic regression when all data points beyond a fixed one are all successes or all fails. So we have data points## ( a_{i1}, a_{i2},.., a_{in} , 0/1) ##, with data points ##a_{ij}##ordered; last input a Boolean and a fixed value for j after which all inputs are 0 or all are 1s. Informally, the logistic curve has to do a sharp turn when all Boolean values are equal beyond a certain point. Thanks.
 
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A sigmoid, thanks. Will check the link.
 
Does the term saturation apply?
 
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Klystron said:
Does the term saturation apply?
It was something like that, thanks.
 
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