上QQ阅读APP看书，第一时间看更新

Perpetual learning

The idea of continued assessment or perpetual learning is an important statistical concept to grasp. Consider learning enhanced skills of perception as a common definition. For example, in statistics, we can refer to the idea of cross-validation. This is a statistical approach for measuring (assessing) a statistical model's performance. This practice involves identifying a set of validation values and then running a model a set number of rounds (continuously), using sample datasets and then averaging the results of each round to ultimately see how good a model (or approach) might be in solving a particular problem or meeting an objective.

The expectation here is that given performance results, adjustments could be made to tweak the model so as to provide the ability to identify insights when used with a real or full population of data. Not only is this concept a practice the data developer should use for refining or fine-tuning a data design or data-driven application process, but this is great life advice in the form of try, learn, adjust, and repeat.

The idea of model assessment is not unique to statistics. Data developers might consider this similar to the act of predicting SQL performance or perhaps the practice of an application walkthrough where an application is validated against the intent and purpose stated within its documented requirements.