Rank importance of predictors using ReliefF or RReliefF algorithm

`[ranks,weights] = relieff(X,y,k)`

`[ranks,weights] = relieff(X,y,k,Name,Value)`

`[`

returns the ranks and weights of predictors for the input data matrix
`ranks`

,`weights`

] = relieff(`X`

,`y`

,`k`

)`X`

and response vector `y`

, using either
the ReliefF or RReliefF algorithm with `k`

nearest
neighbors.

If `y`

is numeric, `relieff`

performs
RReliefF analysis for regression by default. Otherwise, `relieff`

performs ReliefF analysis for classification using `k`

nearest
neighbors per class. For more information on ReliefF and RReliefF, see Algorithms.

Predictor ranks and weights usually depend on

`k`

. If you set`k`

to 1, then the estimates can be unreliable for noisy data. If you set`k`

to a value comparable with the number of observations (rows) in`X`

,`relieff`

can fail to find important predictors. You can start with`k`

=`10`

and investigate the stability and reliability of`relieff`

ranks and weights for various values of`k`

.`relieff`

removes observations with`NaN`

values.

[1] Kononenko, I., E. Simec, and M. Robnik-Sikonja. (1997). “Overcoming the
myopia of inductive learning algorithms with RELIEFF.” Retrieved from CiteSeerX:
`https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.4740`

[2] Robnik-Sikonja, M., and I.
Kononenko. (1997). “An adaptation of Relief for attribute estimation in
regression.” Retrieved from CiteSeerX: `https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.8381`

[3] Robnik-Sikonja, M., and I. Kononenko. (2003). “Theoretical and
empirical analysis of ReliefF and RReliefF.” *Machine
Learning*, 53, 23–69.

`fscnca`

| `fsrnca`

| `knnsearch`

| `pdist2`

| `plotPartialDependence`

| `sequentialfs`