What should the Specialist do to prepare the data for model training?
Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.
Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.
Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.
Apply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.
Explanations:
Quantile binning groups data into categorical bins, which could distort the continuous nature of the features and ignore relationships that are important for the model. This is not a suitable approach for handling varying magnitudes.
The Cartesian product transformation creates new combinations of features, but it does not address the issue of varying magnitudes or scale, which is the primary concern in this case.
Normalization (standardization) ensures that each feature has a mean of 0 and a variance of 1, which eliminates the impact of differing magnitudes and prevents features with larger scales from dominating the model.
The orthogonal sparse bigram (OSB) transformation is typically used for generating features in text or categorical data, and does not address the scaling issue in continuous features with varying magnitudes.
I arrange that the answer is:
Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.