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Z-score normalization: Difference between revisions
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(Created page with "{{see also|Machine learning terms}} ===Introduction== Data normalization in machine learning is a critical preprocessing step that helps boost the performance of many algorithms. Normalization involves scaling data to a specified range or distribution to reduce the impact of differences in scale or units of features. One widely-used technique for normalization is Z-score normalization (also referred to as standardization). ==What is Z-score normalization?== Z-score norm...") |
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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | |||
Data normalization in machine learning is a critical preprocessing step that helps boost the performance of many algorithms. Normalization involves scaling data to a specified range or distribution to reduce the impact of differences in scale or units of features. One widely-used technique for normalization is Z-score normalization (also referred to as standardization). | Data normalization in machine learning is a critical preprocessing step that helps boost the performance of many algorithms. Normalization involves scaling data to a specified range or distribution to reduce the impact of differences in scale or units of features. One widely-used technique for normalization is Z-score normalization (also referred to as standardization). | ||