Binomial family in logistic regression Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsLogistic Regression Cost Function ErrorAre decision tree algorithms linear or nonlinearIntuition for Logistic Regression PerformanceWhat is the role of logistic function in logistic regression?Question about Logistic RegressionLogistic Regression Independent SamplesRe: Logistic RegressionCoefficients from Logistic Regression using Scikit-LearnLogistic regression in python
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Binomial family in logistic regression
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsLogistic Regression Cost Function ErrorAre decision tree algorithms linear or nonlinearIntuition for Logistic Regression PerformanceWhat is the role of logistic function in logistic regression?Question about Logistic RegressionLogistic Regression Independent SamplesRe: Logistic RegressionCoefficients from Logistic Regression using Scikit-LearnLogistic regression in python
$begingroup$
I was asked in an interview why do we use the binomial distribution in logistic regression and how is it related to the class that we are predicting?
Could anyone explain, without any mathematical equations, why do we use binomial instead on any other distribution?
classification logistic-regression distribution
edited Jul 17 '18 at 13:35
Stephen Rauch♦
1,52551330
asked Jul 17 '18 at 12:51
Tejas BawaskarTejas Bawaskar
1
$endgroup$
bumped to the homepage by Community♦ 2 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I was asked in an interview why do we use the binomial distribution in logistic regression and how is it related to the class that we are predicting?
Could anyone explain, without any mathematical equations, why do we use binomial instead on any other distribution?
classification logistic-regression distribution
edited Jul 17 '18 at 13:35
Stephen Rauch♦
1,52551330
asked Jul 17 '18 at 12:51
Tejas BawaskarTejas Bawaskar
1
$endgroup$
bumped to the homepage by Community♦ 2 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I was asked in an interview why do we use the binomial distribution in logistic regression and how is it related to the class that we are predicting?
Could anyone explain, without any mathematical equations, why do we use binomial instead on any other distribution?
classification logistic-regression distribution
edited Jul 17 '18 at 13:35
Stephen Rauch♦
1,52551330
asked Jul 17 '18 at 12:51
Tejas BawaskarTejas Bawaskar
1
$endgroup$
I was asked in an interview why do we use the binomial distribution in logistic regression and how is it related to the class that we are predicting?
Could anyone explain, without any mathematical equations, why do we use binomial instead on any other distribution?
classification logistic-regression distribution
classification logistic-regression distribution
edited Jul 17 '18 at 13:35
Stephen Rauch♦
1,52551330
asked Jul 17 '18 at 12:51
Tejas BawaskarTejas Bawaskar
1
edited Jul 17 '18 at 13:35
Stephen Rauch♦
1,52551330
asked Jul 17 '18 at 12:51
Tejas BawaskarTejas Bawaskar
1
edited Jul 17 '18 at 13:35
Stephen Rauch♦
1,52551330
edited Jul 17 '18 at 13:35
Stephen Rauch♦
1,52551330
edited Jul 17 '18 at 13:35
Stephen Rauch♦
1,52551330
1,52551330
asked Jul 17 '18 at 12:51
Tejas BawaskarTejas Bawaskar
1
asked Jul 17 '18 at 12:51
Tejas BawaskarTejas Bawaskar
1
asked Jul 17 '18 at 12:51
Tejas BawaskarTejas Bawaskar
1
1
bumped to the homepage by Community♦ 2 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 2 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
From wikipedia:
..., the binomial distribution with parameters n and $rho$ is the discrete probability distribution of the number of successes in a sequence of n independent experiments, each asking a yes–no question, and each with its own boolean-valued outcome: a random variable containing a single bit of information: success/yes/true/one (with probability $rho$) or failure/no/false/zero (with probability $rho = 1 − rho$).
So if you know that logistic regression is performed in order to model a binary output variable to some modelling question (i.e. to give 0 or 1, yes or no, etc.), it would make sense to base any probabilistic assumptions on a distribution, which shares this feature. Therefore, a binomial distribution may make sense compared to a continuous distribution, such as a Gaussian or Cauchy.
answered Jul 17 '18 at 19:21
n1k31t4n1k31t4
6,5312421
$endgroup$
add a comment |
$begingroup$
Assume that you have a time variable and you observe at each time and at a certain bus stop if there is a bus arriving or not. Let the probability that a bus arrives at a bus stop at time $t$ be denoted as $p(t)$. This essence of success/failure is a binomial distribution and Logistic regression computes/predicts $p(t)$ by shifting and stretching the logistic curve.
answered Sep 16 '18 at 2:06
Ahmad BazziAhmad Bazzi
1414
$endgroup$
add a comment |
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2 Answers
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oldest
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2 Answers
2
active
oldest
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$begingroup$
From wikipedia:
..., the binomial distribution with parameters n and $rho$ is the discrete probability distribution of the number of successes in a sequence of n independent experiments, each asking a yes–no question, and each with its own boolean-valued outcome: a random variable containing a single bit of information: success/yes/true/one (with probability $rho$) or failure/no/false/zero (with probability $rho = 1 − rho$).
So if you know that logistic regression is performed in order to model a binary output variable to some modelling question (i.e. to give 0 or 1, yes or no, etc.), it would make sense to base any probabilistic assumptions on a distribution, which shares this feature. Therefore, a binomial distribution may make sense compared to a continuous distribution, such as a Gaussian or Cauchy.
answered Jul 17 '18 at 19:21
n1k31t4n1k31t4
6,5312421
$endgroup$
add a comment |
$begingroup$
From wikipedia:
..., the binomial distribution with parameters n and $rho$ is the discrete probability distribution of the number of successes in a sequence of n independent experiments, each asking a yes–no question, and each with its own boolean-valued outcome: a random variable containing a single bit of information: success/yes/true/one (with probability $rho$) or failure/no/false/zero (with probability $rho = 1 − rho$).
So if you know that logistic regression is performed in order to model a binary output variable to some modelling question (i.e. to give 0 or 1, yes or no, etc.), it would make sense to base any probabilistic assumptions on a distribution, which shares this feature. Therefore, a binomial distribution may make sense compared to a continuous distribution, such as a Gaussian or Cauchy.
answered Jul 17 '18 at 19:21
n1k31t4n1k31t4
6,5312421
$endgroup$
add a comment |
$begingroup$
From wikipedia:
..., the binomial distribution with parameters n and $rho$ is the discrete probability distribution of the number of successes in a sequence of n independent experiments, each asking a yes–no question, and each with its own boolean-valued outcome: a random variable containing a single bit of information: success/yes/true/one (with probability $rho$) or failure/no/false/zero (with probability $rho = 1 − rho$).
So if you know that logistic regression is performed in order to model a binary output variable to some modelling question (i.e. to give 0 or 1, yes or no, etc.), it would make sense to base any probabilistic assumptions on a distribution, which shares this feature. Therefore, a binomial distribution may make sense compared to a continuous distribution, such as a Gaussian or Cauchy.
answered Jul 17 '18 at 19:21
n1k31t4n1k31t4
6,5312421
$endgroup$
From wikipedia:
..., the binomial distribution with parameters n and $rho$ is the discrete probability distribution of the number of successes in a sequence of n independent experiments, each asking a yes–no question, and each with its own boolean-valued outcome: a random variable containing a single bit of information: success/yes/true/one (with probability $rho$) or failure/no/false/zero (with probability $rho = 1 − rho$).
So if you know that logistic regression is performed in order to model a binary output variable to some modelling question (i.e. to give 0 or 1, yes or no, etc.), it would make sense to base any probabilistic assumptions on a distribution, which shares this feature. Therefore, a binomial distribution may make sense compared to a continuous distribution, such as a Gaussian or Cauchy.
answered Jul 17 '18 at 19:21
n1k31t4n1k31t4
6,5312421
answered Jul 17 '18 at 19:21
n1k31t4n1k31t4
6,5312421
answered Jul 17 '18 at 19:21
n1k31t4n1k31t4
6,5312421
answered Jul 17 '18 at 19:21
n1k31t4n1k31t4
6,5312421
6,5312421
add a comment |
add a comment |
$begingroup$
Assume that you have a time variable and you observe at each time and at a certain bus stop if there is a bus arriving or not. Let the probability that a bus arrives at a bus stop at time $t$ be denoted as $p(t)$. This essence of success/failure is a binomial distribution and Logistic regression computes/predicts $p(t)$ by shifting and stretching the logistic curve.
answered Sep 16 '18 at 2:06
Ahmad BazziAhmad Bazzi
1414
$endgroup$
add a comment |
$begingroup$
Assume that you have a time variable and you observe at each time and at a certain bus stop if there is a bus arriving or not. Let the probability that a bus arrives at a bus stop at time $t$ be denoted as $p(t)$. This essence of success/failure is a binomial distribution and Logistic regression computes/predicts $p(t)$ by shifting and stretching the logistic curve.
answered Sep 16 '18 at 2:06
Ahmad BazziAhmad Bazzi
1414
$endgroup$
add a comment |
$begingroup$
Assume that you have a time variable and you observe at each time and at a certain bus stop if there is a bus arriving or not. Let the probability that a bus arrives at a bus stop at time $t$ be denoted as $p(t)$. This essence of success/failure is a binomial distribution and Logistic regression computes/predicts $p(t)$ by shifting and stretching the logistic curve.
answered Sep 16 '18 at 2:06
Ahmad BazziAhmad Bazzi
1414
$endgroup$
Assume that you have a time variable and you observe at each time and at a certain bus stop if there is a bus arriving or not. Let the probability that a bus arrives at a bus stop at time $t$ be denoted as $p(t)$. This essence of success/failure is a binomial distribution and Logistic regression computes/predicts $p(t)$ by shifting and stretching the logistic curve.
answered Sep 16 '18 at 2:06
Ahmad BazziAhmad Bazzi
1414
answered Sep 16 '18 at 2:06
Ahmad BazziAhmad Bazzi
1414
answered Sep 16 '18 at 2:06
Ahmad BazziAhmad Bazzi
1414
answered Sep 16 '18 at 2:06
Ahmad BazziAhmad Bazzi
1414
1414
add a comment |
add a comment |
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