Answer-first summary for fast verification
Answer: Standard deviation analysis, which provides a statistical measure of how spread out the data points are around the mean, useful for identifying outliers as those beyond 3 standard deviations., Creating scatter plot visualizations for each pair of features to visually inspect for outliers, despite the method's inability to quantify their impact.
**Correct Option: C. Standard deviation analysis** Standard deviation is the most appropriate method for quantifying the impact of outliers in a dataset, especially under the given constraints. It provides a direct statistical measure of data spread around the mean, identifying outliers as data points lying beyond a certain number of standard deviations. This method is computationally efficient and scale-invariant when the data is standardized. **Why other options are not the best choice:** - **A. Principal component analysis (PCA):** While PCA can reduce dimensionality and may indirectly highlight outliers, it does not directly quantify their impact. - **B. Scatter plot visualization:** Scatter plots can visually indicate outliers but do not provide a quantitative measure of their impact. However, when combined with standard deviation analysis (option E), it offers a comprehensive approach, hence the second correct option. - **D. K-means clustering:** This method is unsuitable as it focuses on grouping data into clusters and is not designed for outlier detection or impact assessment. - **E. Both standard deviation analysis and scatter plot visualization:** While combining both methods provides a comprehensive approach, the question specifically asks for the best single method to quantify the impact of outliers, making standard deviation analysis the primary correct answer.
Author: LeetQuiz Editorial Team
Ultimate access to all questions.
No comments yet.
In the context of preparing a dataset for machine learning, you are tasked with evaluating the impact of outliers on the dataset's distribution. The dataset is large, with high dimensionality, and you are particularly interested in quantifying the effect of outliers rather than just identifying them. Given the constraints of computational efficiency and the need for a statistical measure that directly assesses the spread of data points around the mean, which of the following methods would you choose? Additionally, consider the scenario where the dataset contains features with varying scales, and you need a method that is scale-invariant. Choose the best option.
A
Principal component analysis (PCA), as it reduces dimensionality and can indirectly highlight outliers by showing which components contribute most to the variance.
B
Creating scatter plot visualizations for each pair of features to visually inspect for outliers, despite the method's inability to quantify their impact.
C
Standard deviation analysis, which provides a statistical measure of how spread out the data points are around the mean, useful for identifying outliers as those beyond 3 standard deviations.
D
K-means clustering, which groups data into clusters based on similarity but is not designed for outlier detection or impact assessment.
E
Both standard deviation analysis and scatter plot visualization, to first quantify the impact of outliers and then visually confirm their presence.