Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors. While exploring the data, the Specialist notices that the magnitude of the input features vary greatly. The Specialist does not want variables with a larger magnitude to dominate the model.
What should the Specialist do to prepare the data for model training?
A. Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.
B. Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.
C. Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.
D. Apply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.
What should the Specialist do to prepare the data for model training?
A. Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.
B. Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.
C. Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.
D. Apply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.