generate_ROC_curve: Generate_ROC_curve
In nearinj/RandomForestUtils: A Pipeline for Running Cross Validated Random Forest Models
Description
Usage
Arguments
Value
Description
Generate_ROC_curve
Usage
1
generate_ROC_curve(RF_models, dataset, labels, title)
Arguments
RF_models
A list containing caret RF_models. If using Run_RF_Pipeline this information will be contained within the fifth index of the returned list.
dataset
The feature_table that was input into Run_RF_Pipeline
labels
The actual labels for each sample(row) within the dataset
title
The title of the plot
Value
Returns a ggplot object that plots the testing AUC of each of the best cross-validated random forest models generated when data is split into
test and cross-validation datasets. Note that the redline represents the mean value for the senstivity and specificity at each step tested during ROC calculation
nearinj/RandomForestUtils documentation built on July 30, 2020, 9:51 a.m.
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Description Usage Arguments Value
Description
Generate_ROC_curve
Usage
1 | generate_ROC_curve(RF_models, dataset, labels, title)
|
Arguments
RF_models |
A list containing caret RF_models. If using Run_RF_Pipeline this information will be contained within the fifth index of the returned list. |
dataset |
The feature_table that was input into Run_RF_Pipeline |
labels |
The actual labels for each sample(row) within the dataset |
title |
The title of the plot |
Value
Returns a ggplot object that plots the testing AUC of each of the best cross-validated random forest models generated when data is split into test and cross-validation datasets. Note that the redline represents the mean value for the senstivity and specificity at each step tested during ROC calculation
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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