Hands-On Automated Machine Learning

Big O notation

You have probably heard of big O notation. It has different classes for indicating complexity such as linear—O(n), logarithmic—O(log n), quadratic—O(n2), cubic—O(n3), and similar classes. The reason you use big O is because the runtime of algorithms is highly dependent on the hardware and you need a systematic way of measuring the performance of an algorithm based on the size of its input. Big O looks at the steps of an algorithm and figures out the worst-case scenario as mentioned.

For example, if n is the number of elements that you would like to append to a list, its complexity is O(n), because the number of appended operations depends on the n. The following code block will help you to plot how different complexities grow ...