Answer:
E. Human judgment may be required for individual cases to deal with missing values in records used for building prediction models.
Step-by-step explanation:
Data mining is the process of discovering patterns and trends in large data sets, typically with the goal of predicting future outcomes or identifying important relationships within the data.
There are two main types of data mining: supervised learning and unsupervised learning. In supervised learning, the data are labeled with the correct outcomes, and the algorithm is trained to predict those outcomes based on the input data. In unsupervised learning, the data are not labeled, and the algorithm must discover patterns and relationships within the data without guidance.
One common technique used in data mining is logistic regression, which is appropriate for predicting a binary outcome (e.g., yes/no, pass/fail). This technique is not appropriate for predicting continuous variables, so statement A is incorrect.
Supervised learning is not necessarily less profitable than unsupervised learning. In fact, many data mining applications use supervised learning because it allows for more accurate predictions. Therefore, statement B is incorrect.
Unsupervised learning algorithms do not predict or classify one and only one outcome variable at a time. They are capable of discovering multiple patterns and relationships within the data, so statement C is incorrect.
The more variables included in a model, the greater the risk of overfitting the data. Overfitting occurs when a model is overly complex and captures too much of the random noise in the data, leading to poor generalization to new data. Therefore, statement D is incorrect.
In data mining, it is common to encounter missing values in records. In such cases, human judgment may be required to decide how to handle the missing values in order to build accurate prediction models. Therefore, statement E is correct.