High-Dimensional Data

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.