We have used the software package, Gaussian Emulation Machine for sensitivity analysis (GEM-SA), (Kennedy and O'Hagan, 2006) for performing sensitivity analysis and chosen Rosenbrock function as a test problem.
We used a method of Bayesian analysis of computer code outputs (BACCO) with the Gaussian emulation machine for sensitivity analysis software (GEM-SA) to conduct a long-term SA of DNDC for predicting the annual change of soil organic carbon (dSOC), nitrous oxide emission (N2O) and grain yield of spring wheat.
For compatibility with the machine learning techniques, sensitivity, specificity, PPV, NPV and PA of single features for the validation data were calculated with a threshold in which sensitivity equaled to specificity for the training data.
They concluded that different learning machines have different sensitivity to their parameter settings.
Nonetheless, on most machines the trigger sensitivity can be adjusted, and care should be taken to ensure that the highest possible sensitivity is set, without auto-triggering [ 20].
Modeling complications result from the sensitivity of machine RVs to different settings of machine operating parameters and the difficulty to estimate machine performance in terms of NP under stochastic running conditions.
Due to an extensive machine Error Budget with sensitivity analysis, specific error influences have been identified and accuracy enhancements have been carried out, exemplarily by integrating hydrostatic bearings into a torque drive.
Rule-based inferences would be used to obtain a high sensitivity, and machine learning approaches would be applied to filter out the false positives produced by rule-based methods.
In this paper we demonstrated that dealing with the proportions of positive and negative cases entries in the data sets could increase the sensitivity of machine learning methods for automated case identification.
This indicates that the predictive power of RF does not decrease when we included the data points measured in only one experiment, which is in agreement with a recent benchmarking study on the noise sensitivity of machine learning algorithms in bioactivity modelling (Cortes-Ciriano et al., 2015a).
