We learned that "validating" the output from a machine learning algorithm needs to be approached differently than our typical process validation or even traditional computer system validation approaches. In our traditional validation approaches, we work to demonstrate that the output will consistently remain the same. In machine learning, the output changes as the algorithm learns. What did we learn that we can "validate" to increase the credibility of the algorithm output? $($select one$)$: $q,$

The continuous changes occurring as the algorithm learns

The output from the algorithm

The source of the data that is fed into the algorithm

The problem being solved