Why is Dimensionality a Problem in Cancer Research?
High-dimensional data leads to several challenges:
Computational Complexity: Handling large datasets with thousands of features can be computationally expensive and time-consuming. Overfitting: High-dimensional spaces can make it easy for models to overfit the training data, reducing their ability to generalize to new data. Sparse Data: In high dimensions, data points become sparse, making it difficult to detect meaningful patterns and relationships.
