Difference between revisions of "Datamining: Classification"
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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). | ||
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| + | Four main groups of classification algorithms are: | ||
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| + | 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 10:47, 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