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