In Chapter 6, Predictive Modeling, we discussed a number of predictive modeling examples. The model(s) created is/are based on trained data. In a real-world scenario, new data keeps coming in, for example, online transactions, taxi cab transactions (remember the earlier NYC taxi example), and air flight delay predictions. Therefore, the data model should be checked regularly to ensure that it is still satisfactory and that there is no other better model that could be generated for it. With the latter, a good data scientist would continuously be asking at least four of these questions:
- Is there a different algorithm to consider due to changes of the data?
For example, if the current model is using logistic regression (rxLogit), would the decision tree algorithm more accurate (rxDTree) either due to the size or due to changes...
