Which transformation is suited for finding outliers in a dataset?

Finding outliers in a dataset often requires a thorough examination of the data's distribution and characteristics. The recipe transformation is well-suited for this purpose as it involves a systematic approach to pre-processing data, which can include multiple steps such as normalization, scaling, and the application of statistical techniques to identify anomalies.

In a recipe transformation, you can implement techniques like z-score calculation, interquartile range (IQR) analysis, or other methods that help highlight outliers by establishing thresholds for what is considered "normal" data. By applying a comprehensive set of transformations, you can effectively manipulate the dataset to reveal outliers and understand their nature in the context of the entire dataset.

The other transformation types do not specifically target outlier detection in the same manner. For instance, the slice transformation is more about selecting subsets of data rather than analyzing it for outlier behavior. The filter transformation focuses on removing unwanted data points based on certain criteria but does not typically involve an in-depth analysis to identify outliers systematically. The update transformation is mainly used for modifying existing data within a dataset, which does not provide the necessary analytical framework for identifying anomalies.