RFR_red_MSE: A function that run random forest regression over the model...
In ZhenWei10/Sherry-Chapter1:
Description
Usage
Arguments
Value
Description
A function that calculate the reduction of relative mean squared error when adding the additional features using RFR.
10 folds cross validation is applied.
This is the feature importance in a non-linear regression prediction.
Usage
1
RFR_red_MSE(MM, p = 10, k = 30)
Arguments
MM
a matrix of the model matrix.
p
Number of parallels used.
k
Number of folds of cross validation used.
Value
a vector for the reduction in MSE.
ZhenWei10/Sherry-Chapter1 documentation built on Oct. 31, 2019, 1:48 a.m.
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Description Usage Arguments Value
Description
A function that calculate the reduction of relative mean squared error when adding the additional features using RFR. 10 folds cross validation is applied. This is the feature importance in a non-linear regression prediction.
Usage
1 | RFR_red_MSE(MM, p = 10, k = 30)
|
Arguments
MM |
a matrix of the model matrix. |
p |
Number of parallels used. |
k |
Number of folds of cross validation used. |
Value
a vector for the reduction in MSE.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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