What should you do?

You are working on a Neural Network-based project. The dataset provided to you has columns with different ranges. While preparing the data for model training, you discover that gradient optimization is having difficulty moving weights to a good solution.

What should you do?
A . Use feature construction to combine the strongest features.
B . Use the representation transformation (normalization) technique.
C . Improve the data cleaning step by removing features with missing values.
D . Change the partitioning step to reduce the dimension of the test set and have a larger training set.

Answer: B

Explanation:

https://developers.google.com/machine-learning/data-prep/transform/transform-numeric

– NN models needs features with close ranges

– SGD converges well using features in [0, 1] scale

– The question specifically mention "different ranges"

Documentation – https://developers.google.com/machine-learning/data-prep/transform/transform-numeric