learning.curve.RdFunction to calculate and plot the accuracy, hinge loss and binary cross-entropy loss of a machine learning prediction using the ppi.prediction function.
learning.curve(
ppi_prediction_result,
train_sizes = base::seq(0.1, 1, by = 0.1),
models = "all",
verbose = TRUE,
prob = 0.5,
sum_data = "Median"
)result from the function ppi.prediction
sequence of fraction of training sizes to be used for calculation from >0 to 1
Integer of models used to calculate the loss functions. If "all" then all models as specified by the ensembleSize in ppi.prediction will be used.
boolean, prints detailed informations
probability cutoff to calculate accuracy
summary statistic to plot: 'Mean', 'Median', or 'None'
a list with elements
data("example_ppi_prediction")
plot <- learning.curve(example_ppi_prediction)
#> learning curve for model 1 completed.
#> learning curve for model 2 completed.
#> learning curve for model 3 completed.
#> learning curve for model 4 completed.
#> learning curve for model 5 completed.
#> learning curve for model 6 completed.
#> learning curve for model 7 completed.
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#> learning curve for model 10 completed.
#> learning curve for model 11 completed.
#> learning curve for model 12 completed.
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#> learning curve for model 14 completed.
#> learning curve for model 15 completed.
#> learning curve for model 16 completed.
#> learning curve for model 17 completed.
#> learning curve for model 18 completed.
#> learning curve for model 19 completed.
#> learning curve for model 20 completed.
#> learning curve for model 21 completed.
#> learning curve for model 22 completed.
#> learning curve for model 23 completed.
#> learning curve for model 24 completed.
#> learning curve for model 25 completed.
#> learning curve for model 26 completed.
#> learning curve for model 27 completed.
#> learning curve for model 28 completed.
#> learning curve for model 29 completed.
#> learning curve for model 30 completed.
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#> learning curve for model 50 completed.