Computational efficiency and complexity are important aspects of choosing ML algorithms, since they will dictate the resources needed for model training and scoring in terms of time and memory requirements.
For example, a compute-intensive algorithm will require a longer time to train and optimize its hyperparameters. You will usually distribute the workload among available CPUs or GPUs to reduce the amount of time spent to acceptable levels.
In this section, some algorithms will be examined in terms of these constraints but, before getting into deeper details of ML algorithms, you need to know the basics of the complexity of an algorithm.
The complexity of an algorithm will be based on its input size. For ML algorithms, this could be the number of elements and features. You will usually count the number of operations needed to complete the task in the...
