How to calculate ROC curve in caret Stack Overflow?

How to calculate ROC curve in caret Stack Overflow?

Gets the optimal parameters from the Caret object and the probabilities then calculates a number of metrics and plots including: ROC curves, PR curves, PRG curves, and calibration curves. You can put multiple objects from different models into it to compare the results. Thanks for contributing an answer to Stack Overflow!

How to estimate model accuracy in your using the caret package?

The caret package in R provides a number of methods to estimate the accuracy of a machines learning algorithm. In this post you discover 5 approaches for estimating model performance on unseen data. You will also have access to recipes in R using the caret package for each method, that you can copy and paste into your own project, right now.

How is cross validated accuracy reported in caret?

The cross-validated accuracy is reported. Note that, caret is an optimist, and prefers to report accuracy (proportion of correct classifications) instead of the error that we often considered before (proportion of incorrect classifications). We see that there is a wealth of information stored in the list returned by train ().

How is the default data used in caret?

To illustrate caret, first for classification, we will use the Default data from the ISLR package. We first test-train split the data using createDataPartition. Here we are using 75% of the data for training. At first glance, it might appear as if the use of createDataPartition () is no different than our previous use of sample ().

How can I get the optimal cut off point of the ROC in?

As per documentation the optimal cut-off point is defined as the point where Sensitivity + Specificity is maximal (see MX argument in ?ROC ). You can get the according values as follows (see example in ?ROC ):

What is the cut off value for patient in caret?

I think you confuse the prediction cut-off values (here: patient) with the cut-off value for your x-variable (here: pedal power). In your output you will see the coefficients for the intercept and pedal power variable. You want for example a prediction of above 0.8 to be sure someone is a patient.

How are ROC curves used to assess a classifier?

ROC curves also give us the ability to assess the performance of the classifier over its entire operating range. The most widely-used measure is the area under the curve (AUC). As you can see from Figure 2, the AUC for a classifier with no power, essentially random guessing, is 0.5, because the curve follows the diagonal.

How to get ROC curve from training data?

The training function goes over a range of mtry parameter and calculates the ROC AUC. I would like to see the associated ROC curve — how do I do that? Note: if the method used for sampling is LOOCV, then rfFit will contain a non-null data frame in the rfFit$pred slot, which seems to be exactly what I need.

How is caret used to train a model?

After reading in the data and dividing it into training and test data sets, caret’s trainControl () and expand.grid () functions are used to set up to train the gbm model on all of the combinations of represented in the data frame built by expand.grid (). Then train () function does the actual training and fitting of the model.