Mastering Machine Learning with Spark 2.x

Type I versus type II error

Binary classifiers have intuitive interpretation since they are trying to separate data points into two groups. This sounds simple, but we need to have some notion of measuring the quality of this separation. Furthermore, one important characteristic of a binary classification problem is that, often, the proportion of one group of labels versus the other can be disproportionate. That means the dataset may be imbalanced with respect to one label which necessitates careful interpretation by the data scientist.

Suppose, for example, we are trying to detect the presence of a particular rare disease in a population of 15 million people and we discover that - using a large subset of the population - only 10,000 or 10 ...