How to tune hyperparameters to reliably improve ML model accuracy: a detailed guide
The ML model and its preprocessing are individual for each project: the hyperparameters depend on the data. For example, in the logistic regression algorithm there are different hyperparameters (solver, C, penalty), different combinations of which give different results. Similarly, there are tunable support vector machine parameters: gamma, C. These algorithm hyperparameters are available on the Sklearn free Python library site. However, often a developer has to create their own solutions without relying on ready-made recommendations in order to develop an ML-model with high accuracy, which depends on the best combination of hyperparameters. Read the article about testing various combinations of Grid search with and without the Sklearn library, checking the results with cross-validation and conclusions about the efficiency of utilizing CPU. https://towardsdatascience.com/evaluating-all-possible-combinations-of-hyperparameter