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Answer: To verify the dataset's reliability and appropriateness for building predictive models, To identify and rectify data inconsistencies, missing values, and outliers that could bias the model's predictions
Evaluating data quality is essential in a machine learning project to ensure the data's reliability and suitability for modeling, which directly impacts the model's accuracy and fairness. Identifying and correcting data issues such as inconsistencies, missing values, and outliers (E) is crucial to prevent biased predictions. Ensuring the dataset is appropriate for building predictive models (C) is the primary goal of data quality evaluation. Minimizing storage costs (A) and enhancing dashboard visuals (D) are unrelated to data quality's impact on model performance. While computational efficiency (B) is important, it is secondary to the fundamental need for high-quality, reliable data.
Author: LeetQuiz Editorial Team
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In the context of a machine learning project aimed at predicting customer churn for a telecommunications company, the data science team is in the process of preparing their dataset for model training. The dataset includes customer demographics, service usage patterns, customer service interactions, and billing information collected over the past two years. Given the project's goal to accurately identify at-risk customers, why is evaluating the quality of this dataset critical? Choose the two most important reasons.
A
To minimize the storage costs associated with the dataset
B
To ensure the dataset's features are computationally efficient for model training
C
To verify the dataset's reliability and appropriateness for building predictive models
D
To enhance the visual appeal of the data visualization dashboard
E
To identify and rectify data inconsistencies, missing values, and outliers that could bias the model's predictions
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