general | R Documentation |
General meta-features include general information related to the dataset. It is also known as simple measures.
general(...) ## Default S3 method: general(x, y, features = "all", summary = c("mean", "sd"), ...) ## S3 method for class 'formula' general(formula, data, features = "all", summary = c("mean", "sd"), ...)
... |
Not used. |
x |
A data.frame contained only the input attributes. |
y |
A factor response vector with one label for each row/component of x. |
features |
A list of features names or |
summary |
A list of summarization functions or empty for all values. See
post.processing method to more information. (Default:
|
formula |
A formula to define the class column. |
data |
A data.frame dataset contained the input attributes and class |
The following features are allowed for this method:
Ratio of the number of attributes per the number of instances, also known as dimensionality.
Ratio of the number of categorical attributes per the number of numeric attributes.
Proportion of the classes values (multi-valued).
Ratio of the number of instances per the number of attributes.
Number of attributes.
Number of binary attributes.
Number of categorical attributes.
Number of classes.
Number of instances.
Number of numeric attributes.
Ratio of the number of numeric attributes per the number of categorical attributes.
A list named by the requested meta-features.
Donald Michie, David J. Spiegelhalter, Charles C. Taylor, and John Campbell. Machine Learning, Neural and Statistical Classification, volume 37. Ellis Horwood Upper Saddle River, 1994.
Guido Lindner and Rudi Studer. AST: Support for algorithm selection with a CBR approach. In European Conference on Principles of Data Mining and Knowledge Discovery (PKDD), pages 418 - 423, 1999.
Ciro Castiello, Giovanna Castellano, and Anna M. Fanelli. Meta-data: Characterization of input features for meta-learning. In 2nd International Conference on Modeling Decisions for Artificial Intelligence (MDAI), pages 457 - 468, 2005.
Other meta-features:
clustering()
,
complexity()
,
concept()
,
infotheo()
,
itemset()
,
landmarking()
,
model.based()
,
relative()
,
statistical()
## Extract all metafeatures general(Species ~ ., iris) ## Extract some metafeatures general(iris[1:100, 1:4], iris[1:100, 5], c("nrAttr", "nrClass")) ## Extract all meta-features without summarize prop.class general(Species ~ ., iris, summary=c()) ## Use another summarization functions general(Species ~ ., iris, summary=c("sd","min","iqr"))
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.