Hands-On Deep Learning for IoT

General issues in ML models

When we use this input data for training, validation, and testing, usually, the learning algorithms cannot learn 100% accurately, which involves training, validation, and test error (or loss). There are two types of errors that you may encounter in an ML model:

• Irreducible error
• Reducible error

The irreducible error cannot be reduced even with the most robust and sophisticated model. However, the reducible error, which has two components, called bias and variance, can be reduced. Therefore, to understand the model (that is, prediction errors), we need to focus on bias and variance only. Bias means how far the predicted values are from the actual values. Usually, if the average predicted values are very different ...