AUC calculates the performance across all the thresholds and provides an aggregate measure. The value of AUC ranges from 0 to 1. It means a model with 100% wrong prediction will have an AUC of 0.0, whereas models with 100% correct predictions will have an AUC of 1.0. When to Use AUC A...
[5] D. Powers, “Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation,” Journal of Machine Learning Technologies, vol. 2, no. 1, pp. 37–63, 2011.
Yes, it brings automation, so widely discussed machine intelligence, and other awesome perks. But just because you put it there doesn’t guarantee your project will do well and pay off. So, how would you measure the success of a machine learning model? Various machine learning models — ...
Then the analysis concentrates on the type of changes to a confusion matrix that do not change a measure, therefore, preserve a classifier's evaluation (measure invariance). The result is the measure invariance taxonomy with respect to all relevant label distribution changes in a classification ...
The most common measure of retrospective VS performance is the enrichment factor (EF) of a method applied to a particular benchmark. When a large database of compounds is screened one takes the best scored compounds at the top of the ranked list for further evaluation. The number of experimen...
In this paper, the Slack Based Measure (SBM) model in DEA method is used to measure the relative efficiency values of decision units, and then, eleven machine learning models are used to train the absolute efficient frontier to be applied to the performance prediction of new decisions units. ...
ARE evaluates the error magnitude by determining the ratio of the absolute difference between the estimated values and the actual values to the actual value itself. This measure of error is proportional and shows how much the error is compared to the true value. It is often used in forecasting...
In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep...
We proposed a method of using machine learning with various features for the recognition of Japanese notational variants. We increased 0.06 at the F-measure by specific features using existing dictionaries and character pairs useful for recognizing notational vari- ants and obtained 0.91 at the F-mea...
The AUC of this curve provides a measure of classification performance that includes the full range of possible decision thresholds. Although we used the same pool of original data, causing overlap in training data to build the models, the nature of the seed variation prevents us from using the...