Machine and Deep Learning Solutions for Imbalanced Data
Understand the strengths and weaknesses of machine learning and deep learning methods for classifying imbalanced data. This lesson helps you grasp challenges in rare event prediction and introduces deep learning architectures suited for multivariate time series with imbalanced labels.
We'll cover the following...
Choosing the right computational approach for imbalanced data classification can significantly affect the accuracy and efficacy of predictive models. In this lesson, we explore the strengths and limitations of machine learning and deep learning in this context, aiming to provide insights that will guide our methodological choices in tackling rare event prediction problems.
Machine Learning (ML) is known for its simplicity especially in regards to its interpretability. Machine learning methods are, therefore, usually the first choice for most problems. Deep learning, on the other hand, provides more possibilities.
As we discussed earlier, we might have an imbalanced ...