When developing machine learning models to find patterns in data, researchers across fields typically use separate data sets for model training and testing, which allows them to measure how well their trained models do with new, unseen data. But, due to human error, that line sometimes is inadvertently blurred and data used to test how well the model performs bleeds into data used to train it.
The advancement of computing power over recent decades has led to an explosion of digital data, from traffic cameras monitoring commuter habits to smart refrigerators revealing how and when the average family eats. Both computer scientists and business leaders have taken note of the potential of the data. The information…
Machine learning (ML) has become a critical component of many organizations’ digital transformation strategy. From predicting customer behavior to optimizing business processes, ML algorithms are increasingly being used to make decisions that impact business outcomes. Have you ever wondered how these algorithms arrive at their conclusions? The answer lies in…
Explanation methods that help users understand and trust machine-learning models often describe how much certain features used in the model contribute to its prediction. For example, if a model predicts a patient’s risk of developing cardiac disease, a physician might want to know how strongly the patient’s heart rate data…