In my last few articles, I've looked at a number of ways machine learning can help make predictions. The basic idea is that you create a model using existing data and then ask that model to predict an ...

The outlier detection problem can be defined as follows: given a dataset X, find objects that are considerably dissimilar, exceptional and inconsistent with respect to the remaining majority of ...

Reducing hurdles to clinical trials without compromising the therapeutic promises of peptide candidates becomes an essential step in peptide-based drug design. Machine-learning models are ...

After previously detailing how to examine data files and how to identify and deal with missing data, Dr. James McCaffrey of Microsoft Research now uses a full code sample and step-by-step directions ...

Dr. James McCaffrey from Microsoft Research presents a complete program that uses the Python language LightGBM system to create a custom autoencoder for data anomaly detection. You can easily adapt ...

Comparing die test results with other die on a wafer helps identify outliers, but combining that data with the exact location of an outlier offers a much deeper understanding of what can go wrong and ...