Datamining: Classification: Difference between revisions
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Classification is a data mining task of predicting the value of a categorical variable (target or class) by building a model based on one or more numerical and/or categorical variables (predictors or attributes). | |||
Four main groups of classification algorithms are: | |||
Frequency Table | |||
ZeroR | |||
OneR | |||
Naive Bayesian | |||
Decision Tree | |||
Covariance Matrix | |||
Linear Discriminant Analysis | |||
Logistic Regression | |||
Similarity Functions | |||
K Nearest Neighbors | |||
Others | |||
Artificial Neural Network | |||
Support Vector Machine | |||
Latest revision as of 03:47, 1 August 2017
sumber: http://www.saedsayad.com/classification.htm
Classification is a data mining task of predicting the value of a categorical variable (target or class) by building a model based on one or more numerical and/or categorical variables (predictors or attributes).
Four main groups of classification algorithms are:
Frequency Table
ZeroR
OneR
Naive Bayesian
Decision Tree
Covariance Matrix
Linear Discriminant Analysis
Logistic Regression
Similarity Functions
K Nearest Neighbors
Others
Artificial Neural Network
Support Vector Machine