Binary Classifier with High-Dimensional Data

Requirements Clarification & Assessment

• Objective: Develop a binary classifier for a dataset with 1000 samples and 10,000 features.
• Constraints: The primary constraint is the high dimensionality of the dataset, which poses challenges related to overfitting and computational efficiency.
• Assumptions:
  • The dataset is balanced, meaning the classes are evenly distributed.
  • No missing values or categorical variables are present.
  • The relationships between features are not predefined, so multicollinearity might exist.
• Key Challenges:
  • Curse of Dimensionality: With more features than samples (p >> N), the model is prone to overfitting.
  • Computational Complexity: High dimensionality increases the computational load and storage requirements.
  • Feature Redundancy: Potential multicollinearity among features could affect the model's performance.