Which property can be applied according to the "Detect Class Levels" setting?

The "Detect Class Levels" setting is primarily associated with the class levels count threshold property. This property is utilized to ascertain the number of distinct categories within a categorical variable. When data is being processed, especially in classification tasks, identifying the number of unique classes is critical for proper model training and evaluation.

By setting the class levels count threshold, you can control how many unique values must be present for the variable to be treated as a class variable. This is important because too many class levels can complicate model interpretation and increase the risk of overfitting. Thus, the property effectively aids in data preprocessing by ensuring that only relevant categorical variables—those that meet the class level criteria—are included in the analysis.

The other options do not specifically relate to the function of detecting class levels. For instance, the variable generation property pertains to the creation of new variables from existing data, outlier detection focuses on identifying anomalies in the data, and data validation is concerned with ensuring data quality and integrity. These functions do not directly impact the identification of class levels in the same way that the class levels count threshold does.