It’s very tempting to use mathematical measurements like accuracy, area under the ROC curve, or logarithmic computations as the metric defining success in a machine learning model. But using these ...

Researchers are challenging a long-held assumption that there is a trade-off between accuracy and fairness when using machine learning to make public policy decisions. Carnegie Mellon University ...

We used machine-learning techniques to analyze ... which both improves the accuracy of the model and eliminates less important financial metrics from the model. Furthermore, by bootstrapping ...

As solar energy plays an increasing role in the global power supply, ensuring accurate forecasts of ... in Atmospheric Sciences explores how machine learning and statistical techniques can refine ...

Using a group of validation metrics, their results indicated that their ... In these examples, when used with high spatial resolution images (<1 m), machine learning models achieved an accuracy of ...

Current approaches such as machine learning, deep learning, and sensor-based data collection produce promising findings but have limitations such as uneven diagnostic accuracy and overfitting.