Univariate feature ranking for regression using F-tests
ranks features (predictors) using F-tests. The table idx
= fsrftest(Tbl
,ResponseVarName
)Tbl
contains
predictor variables and a response variable, and ResponseVarName
is the
name of the response variable in Tbl
. The function returns
idx
, which contains the indices of predictors ordered by predictor
importance, meaning idx(1)
is the index of the most important
predictor. You can use idx
to select important predictors for
regression problems.
specifies additional options using one or more name-value pair arguments in addition to
any of the input argument combinations in the previous syntaxes. For example, you can
specify categorical predictors and observation weights.idx
= fsrftest(___,Name,Value
)
If you specify the response variable and predictor variables by using the input argument
formula
, then the variable names in the formula must be both
variable names in Tbl
(Tbl.Properties.VariableNames
)
and valid MATLAB identifiers.
You can verify the variable names in Tbl
by using the isvarname
function. The following code returns logical 1
(true
) for each variable that has a valid variable name.
cellfun(@isvarname,Tbl.Properties.VariableNames)
Tbl
are not valid, then convert them by using the
matlab.lang.makeValidName
function.Tbl.Properties.VariableNames = matlab.lang.makeValidName(Tbl.Properties.VariableNames);
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[2] University of Toronto, Computer Science Department. Delve Datasets.
[3] Nash, Warwick J., ed. The Population Biology of Abalone (Haliotis Species) in Tasmania. 1: Blacklip Abalone (H. Rubra) from the North Coast and the Islands of Bass Strait. Technical Report/Department of Sea Fisheries, Tasmania 48. Taroona: Marine Research Laboratories, 1994.
[4] Waugh, S. "Extending and Benchmarking Cascade-Correlation." PhD Thesis. Computer Science Department, University of Tasmania, 1995.
[5] Lichman, M. UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science, 2013. http://archive.ics.uci.edu/ml.