Explore the significance of data normalization in machine learning model training. Learn how normalization ensures consistent feature scales for enhanced model accuracy and performance.

Question

You need to train and test your model. You prepare data for model training. Several of your numeric features have different scales: the first feature has a minimum value of 0.253 and a max of 0.987, the second one – from 12 to 124, and the last one – from 13545 to 56798. You need to bring them to a common scale.

You decide to normalize your data to address this problem.

Does this decision help you to achieve your goal?

A. Yes
B. No

Answer

A. Yes

Explanation

Yes, normalizing the data helps achieve the goal of bringing numeric features with varying scales onto a common scale. Normalization scales the features, ensuring that each falls within a consistent range, preventing certain features from dominating due to larger scales.

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Alex Lim

Alex Lim is a certified IT Technical Support Architect with over 15 years of experience in designing, implementing, and troubleshooting complex IT systems and networks. He has worked for leading IT companies, such as Microsoft, IBM, and Cisco, providing technical support and solutions to clients across various industries and sectors. Alex has a bachelor’s degree in computer science from the National University of Singapore and a master’s degree in information security from the Massachusetts Institute of Technology. He is also the author of several best-selling books on IT technical support, such as The IT Technical Support Handbook and Troubleshooting IT Systems and Networks. Alex lives in Bandar, Johore, Malaysia with his wife and two chilrdren. You can reach him at [email protected] or follow him on Website | Twitter | Facebook