Optimizing machine learning models

The concept of optimization is integral to an ML model. ML helps make clusters, detect anomalies, predict the future from historical data, and so forth. However, when it comes to minimizing costs in a business, finding optimal placement of business facilities, et cetera, what we need is a mathematical optimization model.

We will talk about optimization in machine learning in this section. Optimization ensures that the structure and configuration of the ML model are as effective as possible to achieve the goal it has been built for. Optimization techniques automate the testing of different model configurations. The best configuration (set of hyperparameters) has the lowest margin of error, thereby yielding the most accurate model for a given dataset. Getting the hyperparameter optimization right for an ML model can be tedious, as both under-optimized (underfit) as well as over-optimized (overfit) models fail. Overfitting is when a model is trained...