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Answer: All of the above, as Spark MLlib supports various data preprocessing techniques.
In a Spark MLlib implementation, various data preprocessing techniques can be applied to improve the quality of the machine learning model. Data cleaning involves handling missing values, outliers, and errors in the dataset, ensuring that the data is clean and reliable. Data transformation converts the data into a suitable format for machine learning models, such as normalization or standardization, which can improve the model's performance. Feature engineering involves creating new features from existing data to improve the model's performance by capturing more relevant information. Spark MLlib supports these data preprocessing techniques, allowing users to choose the appropriate method based on their specific requirements and dataset characteristics.
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In a Spark MLlib implementation, you are working with a large dataset and need to perform data preprocessing to improve the quality of your machine learning model. Which of the following data preprocessing techniques can be applied in Spark MLlib, and how do they work?
A
Data cleaning, which involves handling missing values, outliers, and errors in the dataset.
B
Data transformation, which involves converting the data into a suitable format for machine learning models, such as normalization or standardization.
C
Feature engineering, which involves creating new features from existing data to improve the model's performance.
D
All of the above, as Spark MLlib supports various data preprocessing techniques.
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