How To Install "r-cran-rocr" Package on Ubuntu
Quick Install Instructions of r-cran-rocr on Ubuntu Server. It’s Super Easy! simply click on Copy button to copy the command and paste into your command line terminal using built-in APT package manager.
See below for quick step by step instructions of SSH commands, Copy/Paste to avoid miss-spelling or accidently installing a different package.
See below for quick step by step instructions of SSH commands, Copy/Paste to avoid miss-spelling or accidently installing a different package.
Quick Install Steps:
Step 1
sudo apt-get update -y
Step 2
sudo apt-get install -y r-cran-rocr
Step 3
Check the system logs to confirm that there are no related errors.
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see ZoomAdmin Features for list of features and demo videos. And you can start with the Free Plan.
Execute the commands above step by step.
You can simply hit the copy button to copy the command and paste into the command line interface.
Note: -y flag means to assume yes and silently install, without asking you questions in most cases.
Note: -y flag means to assume yes and silently install, without asking you questions in most cases.
GNU R package to prepare and display ROC curves
ROC graphs, sensitivity/specificity curves, lift charts, and precision/recall plots are popular examples of trade-off visualizations for specific pairs of performance measures. ROCR is a flexible tool for creating cutoff-parametrized 2D performance curves by freely combining two from over 25 performance measures (new performance measures can be added using a standard interface). Curves from different cross-validation or bootstrapping runs can be averaged by different methods, and standard deviations, standard errors or box plots can be used to visualize the variability across the runs. The parametrization can be visualized by printing cutoff values at the corresponding curve positions, or by coloring the curve according to cutoff. All components of a performance plot can be quickly adjusted using a flexible parameter dispatching mechanism. Despite its flexibility, ROCR is easy to use, with only three commands and reasonable default values for all optional parameters. GNU R package to prepare and display ROC curves
ROC graphs, sensitivity/specificity curves, lift charts, and precision/recall plots are popular examples of trade-off visualizations for specific pairs of performance measures. ROCR is a flexible tool for creating cutoff-parametrized 2D performance curves by freely combining two from over 25 performance measures (new performance measures can be added using a standard interface). Curves from different cross-validation or bootstrapping runs can be averaged by different methods, and standard deviations, standard errors or box plots can be used to visualize the variability across the runs. The parametrization can be visualized by printing cutoff values at the corresponding curve positions, or by coloring the curve according to cutoff. All components of a performance plot can be quickly adjusted using a flexible parameter dispatching mechanism. Despite its flexibility, ROCR is easy to use, with only three commands and reasonable default values for all optional parameters.
ROC graphs, sensitivity/specificity curves, lift charts, and precision/recall plots are popular examples of trade-off visualizations for specific pairs of performance measures. ROCR is a flexible tool for creating cutoff-parametrized 2D performance curves by freely combining two from over 25 performance measures (new performance measures can be added using a standard interface). Curves from different cross-validation or bootstrapping runs can be averaged by different methods, and standard deviations, standard errors or box plots can be used to visualize the variability across the runs. The parametrization can be visualized by printing cutoff values at the corresponding curve positions, or by coloring the curve according to cutoff. All components of a performance plot can be quickly adjusted using a flexible parameter dispatching mechanism. Despite its flexibility, ROCR is easy to use, with only three commands and reasonable default values for all optional parameters. GNU R package to prepare and display ROC curves
ROC graphs, sensitivity/specificity curves, lift charts, and precision/recall plots are popular examples of trade-off visualizations for specific pairs of performance measures. ROCR is a flexible tool for creating cutoff-parametrized 2D performance curves by freely combining two from over 25 performance measures (new performance measures can be added using a standard interface). Curves from different cross-validation or bootstrapping runs can be averaged by different methods, and standard deviations, standard errors or box plots can be used to visualize the variability across the runs. The parametrization can be visualized by printing cutoff values at the corresponding curve positions, or by coloring the curve according to cutoff. All components of a performance plot can be quickly adjusted using a flexible parameter dispatching mechanism. Despite its flexibility, ROCR is easy to use, with only three commands and reasonable default values for all optional parameters.
Detailed Instructions:
Step 1
Run update command to update package repositories and get latest package information.
sudo apt-get update -y
Step 2
Run the install command with -y flag to quickly install the packages and dependencies.
sudo apt-get install -y r-cran-rocr
Step 3
Check the system logs to confirm that there are no related errors.
You can use
ZoomAdmin
to check the logs, manager servers, host multiple websites and apps on your servers and more. The apps run in docker containers, to learn more
see ZoomAdmin Features for list of features and demo videos. And you can start with the Free Plan.
see ZoomAdmin Features for list of features and demo videos. And you can start with the Free Plan.