predsc

<p>The Sonar data was split into training (157 points) and 
testing (51 points), a gbm model was fitted using Caret 
on the training data. Then these are the predicted 
probabilities of the model on the test data. A random
forest model was then fit and tested in the same manner. 
The probabilities and ground truth labels were combined 
horizontally for further analysis.</p>

datasets

Straightforward and detailed evaluation of machine learning models. 'MLeval' can produce receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration curves, and PR gain curves. 'MLeval' accepts a data frame of class probabilities and ground truth labels, or, it can automatically interpret the Caret train function results from repeated cross validation, then select the best model and analyse the results. 'MLeval' produces a range of evaluation metrics with confidence intervals.

Christopher John

MLeval

Machine Learning Model Evaluation

predsc function

<p>A data frame with 102 rows as points and 4 variables</p>

predsc: Predictions from gbm and random forest on the Sonar test data

Description

Usage

Arguments

Format