OK, so we're assuming supervised learning. In that case, how were the features of the training set chosen? And how was the training set labelled? Most of the time, outside of semi-supervised learning, those things are…
>> their models have 20,000 vectors in determining credit worthiness. How would you begin to break that down to something explainable? Well, somehow they decided that their 20k-parameter model is accurate. They should…
OK, so we're assuming supervised learning. In that case, how were the features of the training set chosen? And how was the training set labelled? Most of the time, outside of semi-supervised learning, those things are…
>> their models have 20,000 vectors in determining credit worthiness. How would you begin to break that down to something explainable? Well, somehow they decided that their 20k-parameter model is accurate. They should…