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Answer: Using imputation methods to fill in missing values based on the distribution or other statistical measures of the available data.
**Correct Option: D. Using imputation methods to fill in missing values based on the distribution or other statistical measures of the available data.** Imputation is a sophisticated approach that estimates missing values using the available data, preserving the dataset's integrity and volume, which is crucial for training accurate models, especially when the missing data is not randomly distributed. **Why other options are incorrect:** - **A. Normalizing the dataset:** Normalization adjusts the scale of numerical features but does not address missing values. - **B. Implementing ensemble learning:** While ensemble learning can improve model performance, it does not directly handle missing data. - **C. Dropping all rows with missing values:** This approach can lead to significant data loss, especially in large datasets with non-randomly distributed missing values, potentially biasing the model. - **E. Applying data sharding:** Data sharding is unrelated to handling missing values and is more about data distribution in distributed systems. **Note:** The choice of imputation method (e.g., mean, median, mode, or more advanced techniques like predictive modeling) should be carefully considered based on the nature of the data and the missingness pattern.
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In the context of preparing a dataset for machine learning, you encounter a significant amount of missing values across several features. The dataset is large, and the missing values are not randomly distributed. Considering the need to preserve as much data as possible for accurate model training, which of the following methods would be the MOST appropriate to handle the missing values? Choose the best option.
A
Normalizing the dataset to adjust the scale of numerical features, assuming this will indirectly address the missing values.
B
Implementing ensemble learning techniques, expecting that the combination of multiple models will compensate for the missing data.
C
Dropping all rows with missing values to ensure the dataset is clean and ready for model training.
D
Using imputation methods to fill in missing values based on the distribution or other statistical measures of the available data.
E
Applying data sharding to distribute the dataset across multiple nodes, thinking this will mitigate the impact of missing values.