View source: R/ranger_reg_plot.R
plot_perf_VS_rand | R Documentation |
This outputs a histogram and a p-value showing if the performance of a real regression model significantly better than null models.
plot_perf_VS_rand(
x,
y,
predicted_y,
prefix = "train",
target_field,
nfolds,
metric = "MAE",
permutation = 100,
n_features = NA,
outdir = NULL
)
x |
The train data. |
y |
The numeric labeling data. |
predicted_y |
The predicted values for y. |
prefix |
The prefix for the dataset in the training or testing. |
target_field |
A string indicating the target field of the metadata for regression. |
nfolds |
The number of folds in the cross validation. If nfolds > length(y) or nfolds==-1, uses leave-one-out cross-validation. If nfolds was a factor, it means customized folds (e.g., leave-one-group-out cv) were set for CV. |
metric |
The regression performance metric applied, including MAE, RMSE, MSE, R_squared, Adj_R_squared, or Separman_rho. |
permutation |
The permutation times for a random guess of regression performance. |
n_features |
The number of features in the training data. |
outdir |
The output directory. |
Shi Huang
set.seed(123)
x <- data.frame(rbind(t(rmultinom(7, 75, c(.201,.5,.02,.18,.099))),
t(rmultinom(8, 75, c(.201,.4,.12,.18,.099))),
t(rmultinom(15, 75, c(.011,.3,.22,.18,.289))),
t(rmultinom(15, 75, c(.091,.2,.32,.18,.209))),
t(rmultinom(15, 75, c(.001,.1,.42,.18,.299)))))
y<- 1:60
rf_model<-rf.out.of.bag(x, y)
p<-plot_perf_VS_rand(x=x, y=y, predicted_y=rf_model$predicted, prefix="train", nfolds=5,
permutation=100, metric="MAE", target_field="age", n_features=5)
p
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.