Evaluating the performance of the model using different metrics is integral to every data science project. You build a model, get feedback from metrics, make improvements, and continue until you achieve ...

This is a process that helps us figure out where and why the errors occur. Poor quality of predictions made by an ML model does not necessarily mean there is a bug. You have to investigate a broader rang...