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 i

The correct answer is B. Use the representation transformation (normalization) technique.. The presence of feature value X in the formula will affect the step size of the gradient descent. The difference in ranges of features will cause different step sizes for each feature. To ensure that the gradient descent moves smoothly towards the minima and that the steps for gr

Submitted by rachelw· Apr 18, 2026Data processing and feature engineering

Question

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?

Options

AUse feature construction to combine the strongest features.
BUse the representation transformation (normalization) technique.
CImprove the data cleaning step by removing features with missing values.
DChange the partitioning step to reduce the dimension of the test set and have a larger training set.

How the community answered

(33 responses)

A
3% (1)
B
94% (31)
C
3% (1)

Explanation

The presence of feature value X in the formula will affect the step size of the gradient descent. The difference in ranges of features will cause different step sizes for each feature. To ensure that the gradient descent moves smoothly towards the minima and that the steps for gradient descent are updated at the same rate for all the features, we scale the data before feeding it to

Topics

#Data Preprocessing#Feature Scaling#Gradient Optimization#Neural Networks

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