Each ML algorithm has its own strengths and weaknesses. Selecting an appropriate machine algorithm and tuning the model requires a fair amount of experience working with these algorithms, however, the following factors also play a significant role in applying these techniques effectively:
- Asking the right question: A great deal of effort is generally required in formulating the right question.
- Understanding the business domain: Having a good understanding of the relevant business domain and context is equally important to build good models.
- Understanding data: Ultimately, the data is used to train the model. If the data is not understood correctly or the data quality is poor, the built model is unlikely to be effective.
All of the preceding aspects outlined are somewhat interdependent and a mastery of all of these is a prerequisite to selecting the appropriate...
