FMEANone

FMEANone is a term used in machine learning to describe a specific configuration of feature extraction during data processing. It indicates that no mean-based aggregation (averaging) is applied when extracting features from raw data; instead, the features are used directly or through other methods. This setting can affect how models interpret the data, potentially capturing more detailed or specific information rather than summarized averages. In practical terms, choosing FMEANone means you’re opting out of averaging features, which may be useful for preserving unique patterns in the data that could be important for analysis or modeling.