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.