thinQmaster Virtual testing partner Adaptive practice exams that find your weak spots and drill them. Every answer explained. From $69
Enroll

thinQmaster

Find your exam

Five questions free, before you decide.

Step 1 · choose a vendor

In one of our camps? Your practice exams come with the tuition — they are on your study page already.

AI & Machine Learning · Intermediate

Curse of Dimensionality

The set of problems that arise as feature count grows: data becomes sparse and distance measures lose meaning.

Definition

In high-dimensional spaces the volume grows exponentially, so far more data is needed to cover it and points tend to be roughly equidistant. Distance-based algorithms such as k-NN and k-means degrade, and overfitting risk rises. Remedies include feature selection, dimensionality reduction and regularization.

Exam tip

When a question pairs many features with few samples and poor performance, think curse of dimensionality.

Related terms