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Answer: To scale features to a uniform range, thereby enhancing model performance, To encrypt sensitive information within the dataset
**Correct Option: C. To scale features to a uniform range, thereby enhancing model performance** **Explanation:** Data normalization is essential in machine learning to adjust the scale of features so that they contribute equally to the model's learning process. This is particularly important in scenarios where features have different scales, as it prevents features with larger scales from dominating the model's behavior. Normalization techniques, such as Min-Max scaling or Z-score normalization, transform the features to a common scale without distorting differences in the ranges of values or losing information. **Why other options are incorrect:** - **A. To encrypt sensitive information within the dataset:** Data normalization does not involve encryption or any security measures; it's purely a preprocessing step for model performance. - **B. To remove all outliers from the dataset, ensuring uniformity:** While normalization can help in scaling features, it does not inherently remove outliers. Outlier handling is a separate preprocessing step. - **D. To merge datasets from different sources without any discrepancies:** Normalization can make features from different sources more comparable, but it does not address discrepancies in data formats or quality. - **E. Both A and C are correct:** While C is correct, A is not related to the purpose of data normalization, making E an incorrect option.
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In the context of preparing data for machine learning models, data normalization is a critical preprocessing step. Consider a scenario where you are working on a predictive model that includes features with vastly different scales, such as age (ranging from 0 to 100) and income (ranging from 20,000 to 200,000). The model's performance is suboptimal, and you suspect that the disparity in feature scales might be a contributing factor. Which of the following best describes the primary benefit of applying data normalization in this scenario? Choose the best option.
A
To encrypt sensitive information within the dataset
B
To remove all outliers from the dataset, ensuring uniformity
C
To scale features to a uniform range, thereby enhancing model performance
D
To merge datasets from different sources without any discrepancies
E
Both A and C are correct