This section is all about obtaining evaluations, metrics, and supporting Spark ML APIs. In this section, we will go into depth on the importance of the evaluation step regarding quantifiable measures of effectiveness, as follows:
- Our breast cancer classification task is a supervised learning classification problem. In such a problem, there's a so-called true output, and a classifier or ML model generated prediction output for each individual feature measurement or data point in our breast cancer dataset.
- We have now turned our attention to evaluating the performance of our binary classification algorithm by deriving certain metrics. That said, the question is this: ...
