Answer:
The decision tree results may oversimplify reality due to the vast assumptions made in the grouping of the large amounts of data. Some items in the data may be sensitive and prone to change; thus, the results may be wrong regarding this data. Also, the fact that the decisions made are based on expectations, and since there are chances for there to be irrational decisions, can lead to errors. In decision making, there might be oversights leading to bad decisions. Errors in terms of classification as a result of perception differences lead to issues in the final analysis. Due to bulk information, there is also the risk of analysis and decision making may also be affected by classification issues.
This can be dealt with by the use of other assessment tools alongside the decision tree. Also, the information can be classified to ensure personalized review. Additionally, the data can also be organized so that those sensitive or can change with time are dealt with separately to avoid bias. Lastly, it is essential to ensure that the information is broken down into smaller units that are easier to handle, unlike using all the bulky information to make the decisions.