cv.crtree {radiant.model} | R Documentation |
Cross-validation for Classification and Regression Trees
Description
Cross-validation for Classification and Regression Trees
Usage
cv.crtree(
object,
K = 5,
repeats = 1,
cp,
pcp = seq(0, 0.01, length.out = 11),
seed = 1234,
trace = TRUE,
fun,
...
)
Arguments
object |
Object of type "rpart" or "crtree" to use as a starting point for cross validation |
K |
Number of cross validation passes to use |
repeats |
Number of times to repeat the K cross-validation steps |
cp |
Complexity parameter used when building the (e.g., 0.0001) |
pcp |
Complexity parameter to use for pruning |
seed |
Random seed to use as the starting point |
trace |
Print progress |
fun |
Function to use for model evaluation (e.g., auc for classification or RMSE for regression) |
... |
Additional arguments to be passed to 'fun' |
Details
See https://radiant-rstats.github.io/docs/model/crtree.html for an example in Radiant
Value
A data.frame sorted by the mean, sd, min, and max of the performance metric
See Also
crtree
to generate an initial model that can be passed to cv.crtree
Rsq
to calculate an R-squared measure for a regression
RMSE
to calculate the Root Mean Squared Error for a regression
MAE
to calculate the Mean Absolute Error for a regression
auc
to calculate the area under the ROC curve for classification
profit
to calculate profits for classification at a cost/margin threshold
Examples
## Not run:
result <- crtree(dvd, "buy", c("coupon", "purch", "last"))
cv.crtree(result, cp = 0.0001, pcp = seq(0, 0.01, length.out = 11))
cv.crtree(result, cp = 0.0001, pcp = c(0, 0.001, 0.002), fun = profit, cost = 1, margin = 5)
result <- crtree(diamonds, "price", c("carat", "color", "clarity"), type = "regression", cp = 0.001)
cv.crtree(result, cp = 0.001, pcp = seq(0, 0.01, length.out = 11), fun = MAE)
## End(Not run)