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Answer: It ensures the accuracy and reliability of the dataset by correcting errors and handling missing values., It improves the model's performance by normalizing data scales and removing outliers that could skew predictions.
Data cleaning is a foundational step in the machine learning pipeline, especially critical in scenarios like predicting customer churn where data quality directly impacts model accuracy. The correct options highlight its role in ensuring data accuracy and reliability (B) and in enhancing model performance through normalization and outlier removal (E). - **B**: Correcting errors and handling missing values are essential to prevent the model from learning from inaccurate or incomplete data. - **E**: Normalizing data and removing outliers are crucial for models sensitive to the scale of input features and for preventing skewed predictions. **Incorrect Options Analysis**: - **A**: While removing unnecessary columns can reduce computational overhead, it's not the primary purpose of data cleaning. - **C**: Data cleaning does not replace the need for feature engineering; both are distinct and necessary steps. - **D**: Compliance with data protection regulations requires more than just data cleaning; it involves a comprehensive data governance strategy.
Author: LeetQuiz Editorial Team
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In the context of preparing a dataset for a machine learning project aimed at predicting customer churn for a telecommunications company, why is data cleaning considered a crucial step? Choose the two most accurate statements from the options below. (Choose two)
A
It simplifies the dataset by removing unnecessary columns, thereby reducing computational overhead.
B
It ensures the accuracy and reliability of the dataset by correcting errors and handling missing values.
C
It eliminates the need for feature engineering by automatically selecting the most relevant features.
D
It guarantees the dataset's compliance with global data protection regulations without further checks.
E
It improves the model's performance by normalizing data scales and removing outliers that could skew predictions.
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