Computational complexity of ML algorithms When the amount of d | Big Data Science

Computational complexity of ML algorithms
When the amount of data is low, almost any ML algorithm gives acceptable accuracy and is suitable for solving the tasks. But when the volume and size of the data become large, it is necessary to choose an algorithm for training the ML model that does not require too many computing resources. It is better to choose a simple or less expensive algorithm in terms of computation than an algorithm that requires large computational resources, when the accuracy of prediction and evaluation of results is similar or even slightly worse.
The choice of algorithm depends on the following consequences:
• the order of time (complexity of time) required to calculate the algorithm - functions associated with the data of the algorithm itself, the volume and number of features
• set of computational space (spatial complexity) - the order of the space required during the calculation of the algorithm - a function associated with the algorithm, such as the number of features, coefficients, hidden layers of neural networks. Space complexity includes both the size of the input data and the ancillary space (auxiliary space) used by the algorithm during execution;
For example, Mergesort has an ancillary space 𝑂(𝑛) and volume complexity 𝑂(𝑛), while Quicksort has an ancillary space 𝑂(1) and volume complexity 𝑂(𝑛). As a result, both merge sort and quick sort have time stability 𝑂(𝑛log𝑛).
https://medium.com/datadailyread/computational-complexity-of-machine-learning-algorithms-16e7ffcafa7d