CODEWITHSUNDEEP
rclassificationsvmroc
Vash
Vash

Reputation: 1787

ROCR Package - Classification algo other than logistic regression

I was referring to this link which explains the usage of ROCR package for plotting ROC curves and other related accuracy measurement metrics. The author mentions about logistic regression in the beginning, but do these functions(prediction, performance from ROCR) apply to other classification algorithms like SVM, Decision Trees, etc. ?

I tried using prediction() function with results of my SVM model, but it threw a format error despite the arguments being of same type and dimensions. Also I am not sure that if we try coming up with ROC curves for these algorithms, we would get a shape like the one we generally see with logistic regression(like this).

Upvotes: 1

Views: 215

Answers (1)

jruf003
jruf003

Reputation: 1042

The prediction and performance functions are model-agnostic in the sense that they only require the user to input actual and predicted values from a binary classifier. (More precisely, this is what prediction requires, and performance takes as input an object output by prediction). Therefore, you should be able to use both functions for any classification algorithm that can output this information - including both logistic regression and SVM.

Having said this, model predictions can come in different formats (e.g., propensity scores vs. classes; results stored as numeric vs. factor), and you'll need to ensure that what you feed into prediction is appropriate. This can be quite specific, for example, while the predictions argument can represent continuous or class information, it can’t be a factor. See the “details” section of the function’s help file for more information.

Upvotes: 3

Related Questions