How will I know whether machine learning can assist me in resolving an issue?

The majority of machine learning algorithms assume that your training data is one-of-a-kind. Thus, it’s important to randomly divide your test set into training and test sets to guarantee that it represents the population.

You don’t want to retrain your model and assess it on a random test set since it might lead to overly optimistic projections. Use k-fold cross-validation or layered cross-validation as an option.

Several causes can be blamed for poor performance, including:

  • Distorted perception.
  • There are too many distractions.
  • A wide range of anomalies may be found in any population.
  • You need more comprehensive  training materials.