Model best practices
Model accuracy and performance are critical to success for any machine learning and deep learning project. If a model is not accurate enough, the associated business use case will not be successful. Therefore, it is important to focus on model accuracy and performance to increase the chances of success. There are a number of factors that impact model accuracy and performance, so it is important to understand all of them in order to optimize accuracy and performance. Below we list some of the model best practices that can help us leverage best from our model development workflow.
Baseline models
A baseline model is a tool used in machine learning to evaluate other models. It is usually the simplest possible model, and acts as a comparison point for more complex models. The goal is to see if the more complex models are actually providing any improvements over the baseline model. If not, then there is no point in using the more complex model. Baseline models...
