Evaluating All Possible Combinations of Hyperparameters -Grid Search-

The model and the preprocessing are individual for each project. Hyperparameters are tuned according to the dataset and using the same hyperparameters for each project compromises the accuracy of the results. For example, there are different hyperparameters such as'solver', 'C', 'penalty' in the Logistic Regression algorithm, and different combinations of these give different results. Similarly, there are adjustable parameters for Support Vector Machine such as gamma value, C value, and combination of them also gives different results. These hyperparameters of the algorithms are available on the sklearn website.