What is the main purpose of data normalization in machine learning?
- To ensure numerical stability and bring features to a comparable scale
- To increase the accuracy of the model
- To increase the size of the dataset
- To reduce the computation time
Data normalization is a technique often applied as part of data preprocessing in machine learning to bring features to a comparable scale. This is done to ensure numerical stability during the computation and to prevent certain features from dominating others due to their scale.
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