Univariate feature ranking for regression using
*F*-tests

ranks features (predictors) using `idx`

= fsrftest(`Tbl`

,`ResponseVarName`

)*F*-tests. The table `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.If the variable names incellfun(@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);

[1] Rasmussen, C. E., R. M. Neal, G. E. Hinton, D. van Camp, M. Revow, Z. Ghahramani, R. Kustra, and R. Tibshirani. The DELVE Manual, 1996.

[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.