Software engineering

Model Trade-off
Often, it’s a trade-off between different factors when selecting a model. ANN, SVM, and some ensemble methods can be used to create very accurate predictive models, but they may lack simplicity and interpretability and may take a significant amount of resources to train.
In terms of selecting the final model, models with lower interpretability may be preferred when predictive performance is the most important goal, and it’s not necessary to explain how the model works and makes predictions. In some cases, however, model interpretability is mandatory.
Interpretability-driven examples are often seen in the financial industry. In many cases, choosing a machine learning algorithm has less to do with the optimization or the technical aspects of the algorithm and more to do with business decisions. Suppose a machine learning algorithm is used to accept or reject an individual’s credit card application. If the applicant is rejected and decides to file a complaint or take legal action, the financial institution will need to explain how that
decision was made. While that can be nearly impossible for ANN, it’s relatively straightforward for decision tree-based models.
Different classes of models are good at modeling different types of underlying patterns in data. So a good first step is to quickly test out a few different classes of models to know which ones capture the underlying structure of the dataset most efficiently. We will follow this approach while performing model selection in all our supervised learning-based case studies.