Cuestionario sobre curvas de aprendizaje y curvas de validación

Las respuestas y explicaciones aparecen a medida que realiza el cuestionario.

1. In a common data-size learning curve, validation score is plotted against what?
   • Training examples used for each fit
   • Optimization epochs with a fixed training sample
   • Values of one regularization setting
   • Decision thresholds applied to one fitted model
2. Training and validation scores are both low and close. Which diagnosis fits?
   • The model must be well calibrated
   • The model may be underfitting
   • The model must be overfitting because the gap is small
   • More training examples are guaranteed to solve the problem
3. Training performance is strong while validation performance is much weaker. What does the gap suggest?
   • The data distributions must match perfectly
   • The model must be too simple to fit training examples
   • Additional training epochs always remove the gap
   • Overfitting risk
4. What does a validation curve vary?
   • The final test labels used for scoring
   • The number of examples while all model settings stay fixed
   • A hyperparameter such as regularization strength
   • The class names without changing any model settings
5. Why keep a test set separate from repeated tuning?
   • Test labels are needed for every tuning choice
   • Repeated choices can adapt to the validation set
   • A held-out test set automatically repairs training leakage
   • Validation and test sets serve identical roles during selection
6. Validation score still improves at the largest training subset. What is the careful inference?
   • More similar representative data may help, without guarantee
   • The deployed test score must improve by the same amount
   • The remaining improvement must come entirely from more epochs
   • The trend establishes calibrated probabilities
7. Which evaluation split suits a model intended to predict future time-series values?
   • Randomly shuffled timestamps across both partitions
   • Rows ordered only after a random split
   • A chronology-respecting split
   • Later examples used to evaluate earlier predictions retrospectively
8. Why repeat curve points or show uncertainty for a small dataset?
   • To guarantee that each candidate has the same training score
   • To eliminate the need for a final test set
   • To turn score differences into causal effects
   • To reveal variability from the data split