Description Usage Arguments Value Examples
View source: R/predictive.acc.R
Predictive performance across all trees
1 2 | predictive.acc(object = "mfOutput", newdata = F, prob_cutoff = NULL,
plot = T)
|
object |
An object of class |
newdata |
A logical value specifying if the performance needs to be
summarized on test data supplied as |
prob_cutoff |
Predicted probabilities converted into classes (Yes/No, 1/0) based on this probability threshold. Only used for producing predicted Vs actual classes table. |
plot |
A logical value specifying if the use wishes to view performance plots |
A list with performance parameters
oob_r2 |
A vector of predictive accuracy estimates (ranging between 0 and 1) measured on Out-of-bag cases for each tree |
oob_mse |
A vector of MSE for Out-of-bag data for each tree. Valid only if the outcome is continuous. |
oob_overall_r2 |
Overall predictive accuracy measured by combining Out-of-bag predictions across trees. |
oob_overall_mse |
Overall MSE measured by combining Out-of-bag predictions across trees. |
general_r2 |
A vector of predictive accuracy (ranging between 0 and 1) measured on complete learning data for each tree |
general_mse |
A vector of MSE measured on complete learning data for each tree. Valid only if the outcome is continuous. |
general_overall_r2 |
Overall predictive accuracy measured by combining predictions across trees. |
general_overall_mse |
Overall MSE measured by combining predictions across trees. Valid only if the outcome is continuous. |
model_used |
The node model and partition variables used for analysis |
family |
Error distribution assumptions of the model |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ## Not run:
library(mlbench)
set.seed(1111)
# Random Forest analysis of model based recursive partitioning load data
data("BostonHousing", package = "mlbench")
BostonHousing <- BostonHousing[1:90, c("rad", "tax", "crim", "medv", "lstat")]
# Recursive partitioning based on linear regression model medv ~ lstat with 3
# trees. 1 core/processor used.
rfout <- mobforest.analysis(as.formula(medv ~ lstat), c("rad", "tax", "crim"),
mobforest_controls = mobforest.control(ntree = 3, mtry = 2, replace = T,
alpha = 0.05, bonferroni = T, minsplit = 25), data = BostonHousing,
processors = 1, model = linearModel, seed = 1111)
# get predictive performance estimates and produce a performance plot
pacc <- predictive.acc(rfout)
## End(Not run)
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