Difference between revisions of "Association Rule Learning"

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(Created page with "[http://www.youtube.com/results?search_query=Ridge+Regression+artificial+intelligence YouTube search...] [http://www.google.com/search?q=Ridge+Regression+machine+learning+ML ....")
 
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[http://www.youtube.com/results?search_query=Ridge+Regression+artificial+intelligence YouTube search...]
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[http://www.youtube.com/results?search_query=Association+Rule+Learning YouTube search...]
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[http://www.google.com/search?q=Ridge+Regression+machine+learning+ML ...Google search]
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[http://www.google.com/search?q=Association+Rule+Learning+machine+learning+ML ...Google search]
  
 
* [[AI Solver]]
 
* [[AI Solver]]
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** [[...predict values]]
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** [[...predict categories]]
 
* [[Capabilities]]  
 
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* [[Linear Regression]]
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* [http://en.wikipedia.org/wiki/Association_rule_learning Association Rule Learning | Wikipedia]
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* [[Regularization]]
 
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* [[Logistic Regression (LR)]]
 
  
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or Tikhonov Regularization, is the most commonly used regression algorithm to approximate an answer for an equation with no unique solution. This type of problem is very common in machine learning tasks, where the "best" solution must be chosen using limited data. Simply, [Regularization]] introduces additional information to an problem to choose the "best" solution for it. This algorithm is used for analyzing multiple regression data that suffer from multicollinearity. Multicollinearity, or collinearity, is the existence of near-linear relationships among the independent variables. When multicollinearity occurs, least squares estimates are unbiased, but their variances are large so they may be far from the true value. By adding a degree of bias to the regression estimates, ridge regression reduces the standard errors.
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a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness. This rule-based approach also generates new rules as it analyzes more data. The ultimate goal, assuming a large enough dataset, is to help a machine mimic the human brain’s feature extraction and abstract association capabilities from new uncategorized data.
  
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http://cdn-images-1.medium.com/max/600/0*5hhRo51IxvuvU0TT.png
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http://annalyzin.files.wordpress.com/2016/04/association-rules-network-graph2.png
  
  
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<youtube>Q81RR3yKn30</youtube>
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<youtube>WGlMlS_Yydk</youtube>
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<youtube>a7pYbjCYGuI</youtube>
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<youtube>tY1JE6XFjCY</youtube>
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<youtube>YgnpqrgKTbE</youtube>
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== Market Basket Analysis ==
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<youtube>b5hgDPa7a2k</youtube>

Revision as of 09:36, 8 January 2019

YouTube search... ...Google search

a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness. This rule-based approach also generates new rules as it analyzes more data. The ultimate goal, assuming a large enough dataset, is to help a machine mimic the human brain’s feature extraction and abstract association capabilities from new uncategorized data.

association-rules-network-graph2.png


Market Basket Analysis

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