As we have used a known weight vector and intercept to generate the training data in our producer application, we would expect our model to eventually learn this underlying weight vector (in the absence of random noise, which we do not add for this example).
Therefore, we should see the model's error rate decrease over time, as it sees more and more data. We can also use standard regression error metrics to compare the performance of multiple models.
In this example, we will create two models with different learning rates, training them both on the same data stream. We will then make predictions for each model and measure the mean-squared error (MSE) and root mean-squared error (RMSE) metrics ...
