Evaluating the model in Python

In the previous recipes, we have trained the regression model using the Linear Learner algorithm and loaded the model using MXNet and Gluon. After the training step, the model needs to be evaluated, and the results and metric values need to be compared with other models. Model evaluation is a critical part of the ML process as this helps us find the best model, which will be used to perform predictions on future unseen values. This recipe aims to provide a simplified set of steps when evaluating regression models.

With the Python programming language, we will generate the visualization of the regression line over the original scatter plot chart and evaluate the ML model using the relevant metrics (for example, Root Mean Squared Error(RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE))

Getting ready

Here are the prerequisites for this recipe:

• This recipe continues on from Loading a linear learner model with Apache MXNet in...