What is high dimensionality?
In high-dimensional datasets, a lot of attributes are entered, any of which might be useful in modelling the prediction. Nevertheless, these large numbers of features also have disadvantages such as sparsity, which means that data points are spread out and the risk of overfitting is higher.
Example: In e-commerce, a high-dimensional dataset might include many customer attributes such as age, purchase frequency, browsing history, and location. While these features provide valuable insights for recommendation systems, but the abundance of dimensions can make it difficult to separate real purchasing trends and randomly occurring buying episodes.
The Relationship Between High Dimensionality and Overfitting
Overfitting occurs when a model becomes overly complex and instead of learning the underlying patterns, it starts to memorize noise in the training data. With high dimensionality, where datasets have a large number of features, this problem further intensifies. Let’s explore how high dimensionality and overfitting are related.