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Data Interview Question

High-Dimensional Data

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Requirements Clarification & Assessment

  1. Understanding the Dataset:

    • Number of Instances: Evaluate if the number of instances is significantly larger than the number of features (n >> p). If not, consider collecting more data to ensure the robustness of the model.
    • Feature Relevance: Assess if all 500 features are relevant to the problem at hand. This involves domain knowledge to determine which features are likely to be informative.
  2. Problem Context:

    • Objective: Clearly define the objective of the analysis or model. Is it prediction, classification, or exploratory analysis?
    • Computational Resources: Consider the computational resources available, as high-dimensional datasets can be resource-intensive.
  3. Data Quality:

    • Missing Values: Check for missing values and decide on imputation methods.
    • Data Types: Verify that data types are correctly assigned and consistent across the dataset.
  4. Feature Relationships:

    • Correlation: Identify highly correlated features that may lead to multicollinearity issues.
    • Redundancy: Look for redundant features that do not add value to the model.