« Recherche de règles d'association » : différence entre les versions


(Page créée avec « discovering rules for observing A given B. This is closely related to clustering in that we’re attempting to find connections between events. However, the difference is... »)
 
Aucun résumé des modifications
Balise : Éditeur de wikicode 2017
Ligne 1 : Ligne 1 :
== en construction ==
[[Catégorie:Intelligence artificielle]]
[[Catégorie:vocabulary]]
== Définition ==
== Français ==
'''XXXXXXXXXXXXXXX '''
== Anglais ==
'''XXXXXXXXXXXXXXX '''
discovering rules for observing A given B. This is closely related to clustering in that we’re attempting to find connections between events. However, the difference is the approach. Instead of drawing bounds on a region and seeing if someone fits in that bucket, we use the frequency of a collection of discrete observations to create priors: what’s the probability of observing A, or B, or C? From these, we figure out what the probability is of observing A given B, or observing C given A and B. This is just Bayesian statistics: P(A), P(B | A), P(C | A, B), and so forth.
discovering rules for observing A given B. This is closely related to clustering in that we’re attempting to find connections between events. However, the difference is the approach. Instead of drawing bounds on a region and seeing if someone fits in that bucket, we use the frequency of a collection of discrete observations to create priors: what’s the probability of observing A, or B, or C? From these, we figure out what the probability is of observing A given B, or observing C given A and B. This is just Bayesian statistics: P(A), P(B | A), P(C | A, B), and so forth.
<small>


Machine Learning for Beginners – a How-to Guide
Machine Learning for Beginners – a How-to Guide
https://opendatascience.com/machine-learning-for-beginners/
https://opendatascience.com/machine-learning-for-beginners/

Version du 16 août 2019 à 22:48

en construction


Définition

Français

XXXXXXXXXXXXXXX

Anglais

XXXXXXXXXXXXXXX

discovering rules for observing A given B. This is closely related to clustering in that we’re attempting to find connections between events. However, the difference is the approach. Instead of drawing bounds on a region and seeing if someone fits in that bucket, we use the frequency of a collection of discrete observations to create priors: what’s the probability of observing A, or B, or C? From these, we figure out what the probability is of observing A given B, or observing C given A and B. This is just Bayesian statistics: P(A), P(B | A), P(C | A, B), and so forth.


Machine Learning for Beginners – a How-to Guide https://opendatascience.com/machine-learning-for-beginners/