There is mismatch between test predictions(black crosses) and test outputs(red circles), this indicates model has not learned effectively.

There is a significant mismatch between test outputs and test predictions. Showing that the model hasn't learned well.

The test predictions are far from the test outputs, indicating that the model hasn't learned effectively without any parameter updates.

The test predictions should align more closely to the test outputs, showing that the model has learned from the data, minimizing the cost. Predictions improved compared to the "without optimizer case".

The test predictions aligned very well with the test outputs showing the impact of an optimizer.

The test predictions should align closely with the test outputs, showing the effectiveness of the optimizer in minimizing the error and improving the model’s performance.

The test predictions are similar to the graph with gradient descent optimizer. However, because gradient descent is being applied manually, there may be minor differences in performance due to potential variations in step sizes or gradient accuracy. 

The results should be similar to the automatic gradient descent optimizer, but there may be slight differences in performance due to manual updates and the precision of gradient calculations.

The predictions here should be similar to those in the automatic optimizer case, but with potential small differences due to the manual application of gradient descent.