Which two types of data values are regarded as invalid in MODELER?

The selection of undefined values represented by $null$ as invalid in MODELER is rooted in how data integrity and analysis processes are handled. In predictive analytics and data modeling, a $null$ value indicates that a particular data point is missing or unknown. This absence of data can significantly affect model training and predictions, leading to inaccuracies or misleading results.

In modeling practices, handling $null$ effectively is crucial since they cannot be directly interpreted or processed in mathematical and statistical operations. Therefore, the presence of $null$ values is considered an indication of incomplete data entries, making them invalid for inclusion in comprehensive analysis and predictive modeling.

Understanding the nature of data quality is fundamental. While certain data might be technically numeric or possibly formatted correctly, if they lack a defined value, they cannot contribute effectively to the model’s functionality. This is why options that imply values that can still be operational or that represent formatting issues (like white space) are not categorized in the same manner as $null$, which represents a complete absence of data.