Difference between revisions of "Poisson Regression"
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[http://www.google.com/search?q=Poisson+Regression+deep+machine+learning+ML+artificial+intelligence ...Google search] | [http://www.google.com/search?q=Poisson+Regression+deep+machine+learning+ML+artificial+intelligence ...Google search] | ||
| − | * [[AI Solver]] | + | * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]] |
** [[...predict values]] | ** [[...predict values]] | ||
* [[Regression]] Analysis | * [[Regression]] Analysis | ||
| − | * [[Math for Intelligence]] ... [[Finding Paul Revere]] | + | * [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]] |
* [http://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/poisson-regression Poisson Regression? | Microsoft] | * [http://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/poisson-regression Poisson Regression? | Microsoft] | ||
* [http://statisticsbyjim.com/regression/choosing-regression-analysis/ Choosing the Correct Type of Regression Analysis | Jim Frost] | * [http://statisticsbyjim.com/regression/choosing-regression-analysis/ Choosing the Correct Type of Regression Analysis | Jim Frost] | ||
Revision as of 20:14, 13 July 2023
YouTube search... ...Google search
- AI Solver ... Algorithms ... Administration ... Model Search ... Discriminative vs. Generative ... Optimizer ... Train, Validate, and Test
- Regression Analysis
- Math for Intelligence ... Finding Paul Revere ... Social Network Analysis (SNA) ... Dot Product ... Kernel Trick
- Poisson Regression? | Microsoft
- Choosing the Correct Type of Regression Analysis | Jim Frost
Poisson regression is used to model response variables (Y-values) that are counts. It tells you which explanatory variables have a statistically significant effect on the response variable. In other words, it tells you which X-values work on the Y-value. It’s best used for rare events, as these tend to follow a Poisson distribution (as opposed to more common events which tend to be normally distributed). For example:
- Number of colds contracted on airplanes.
- Number of bacteria found in a petri dish.
- Counts of catastrophic computer failures at a large tech firm in a calendar year.
- Number of 911 calls that end in the death of a suspect.
For large means, the normal distribution is a good approximation for the Poisson distribution. Therefore, Poisson regression is more suited to cases where the response variable is a small integer.
Poisson regression is only used for numerical, continuous data. The same technique can be used for modeling categorical explanatory variables or counts in the cells of a contingency table. When used in this way, the models are called loglinear models. What is Poisson Regression? | Statistics How To
