cv.crtree | R Documentation |
Cross-validation for Classification and Regression Trees
cv.crtree(
object,
K = 5,
repeats = 1,
cp,
pcp = seq(0, 0.01, length.out = 11),
seed = 1234,
trace = TRUE,
fun,
...
)
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' |
See https://radiant-rstats.github.io/docs/model/crtree.html for an example in Radiant
A data.frame sorted by the mean, sd, min, and max of the performance metric
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
## 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)
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