Description Usage Arguments Details Author(s) See Also Examples
Ancillary functions for backwards selection
1 2 3 4 5 6 7 8 9 10 11 12 13 | pickSizeTolerance(x, metric, tol = 1.5, maximize)
pickSizeBest(x, metric, maximize)
pickVars(y, size)
caretFuncs
lmFuncs
rfFuncs
treebagFuncs
ldaFuncs
nbFuncs
gamFuncs
lrFuncs
|
x |
a matrix or data frame with the performance metric of interest |
metric |
a character string with the name of the performance metric that should be used to choose the appropriate number of variables |
maximize |
a logical; should the metric be maximized? |
tol |
a scalar to denote the acceptable difference in optimal performance (see Details below) |
y |
a list of data frames with variables |
size |
an integer for the number of variables to retain |
This page describes the functions that are used in backwards selection (aka recursive
feature elimination). The functions described here are passed to the algorithm via the
functions
argument of rfeControl
.
See rfeControl
for details on how these functions should be defined.
The 'pick' functions are used to find the appropriate subset size for
different situations. pickBest
will find the position
associated with the numerically best value (see the maximize
argument to help define this).
pickSizeTolerance
picks the lowest position (i.e. the smallest
subset size) that has no more of an X percent loss in
performances. When maximizing, it calculates (O-X)/O*100, where X is
the set of performance values and O is max(X). This is the percent
loss. When X is to be minimized, it uses (X-O)/O*100 (so that values
greater than X have a positive "loss"). The function finds the
smallest subset size that has a percent loss less than tol
.
Both of the 'pick' functions assume that the data are sorted from smallest subset size to largest.
Max Kuhn
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | ## For picking subset sizes:
## Minimize the RMSE
example <- data.frame(RMSE = c(1.2, 1.1, 1.05, 1.01, 1.01, 1.03, 1.00),
Variables = 1:7)
## Percent Loss in performance (positive)
example$PctLoss <- (example$RMSE - min(example$RMSE))/min(example$RMSE)*100
xyplot(RMSE ~ Variables, data= example)
xyplot(PctLoss ~ Variables, data= example)
absoluteBest <- pickSizeBest(example, metric = "RMSE", maximize = FALSE)
within5Pct <- pickSizeTolerance(example, metric = "RMSE", maximize = FALSE)
cat("numerically optimal:",
example$RMSE[absoluteBest],
"RMSE in position",
absoluteBest, "\n")
cat("Accepting a 1.5 pct loss:",
example$RMSE[within5Pct],
"RMSE in position",
within5Pct, "\n")
## Example where we would like to maximize
example2 <- data.frame(Rsquared = c(0.4, 0.6, 0.94, 0.95, 0.95, 0.95, 0.95),
Variables = 1:7)
## Percent Loss in performance (positive)
example2$PctLoss <- (max(example2$Rsquared) - example2$Rsquared)/max(example2$Rsquared)*100
xyplot(Rsquared ~ Variables, data= example2)
xyplot(PctLoss ~ Variables, data= example2)
absoluteBest2 <- pickSizeBest(example2, metric = "Rsquared", maximize = TRUE)
within5Pct2 <- pickSizeTolerance(example2, metric = "Rsquared", maximize = TRUE)
cat("numerically optimal:",
example2$Rsquared[absoluteBest2],
"R^2 in position",
absoluteBest2, "\n")
cat("Accepting a 1.5 pct loss:",
example2$Rsquared[within5Pct2],
"R^2 in position",
within5Pct2, "\n")
|
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