Prueba de regresión Softmax para clasificación multiclase

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

1. ¿Qué restricción satisfacen las probabilidades softmax para un caso en todas las clases modeladas?
   • They sum to one for each case across modeled classes
   • They sum to one only when the highest-scoring class is correct
   • Their sum equals the number of classes
   • Each class score is converted independently without a shared denominator
2. Un modelo de tres clases arroja probabilidades 0,6, 0,3 y 0,1. ¿Qué clase es la predicción habitual de máxima probabilidad?
   • The first class, because it has the largest probability
   • The second class, because it has the largest probability
   • The third class, because it appears last
   • No class can be chosen until probabilities are converted to integer scores
3. ¿Por qué agregar la misma constante a cada logit de clase deja las probabilidades softmax sin cambios?
   • Adding a constant changes only the denominator
   • Adding a constant changes only the numerator
   • The common exponential factor cancels between numerator and denominator
   • Softmax is invariant only when every logit is multiplied by the same constant
4. ¿Qué implica el supuesto de producción de suma a uno acerca de la configuración objetivo?
   • Several labels may be true together and are modeled independently
   • The classes are ordered and their numeric gaps are meaningful
   • The output may omit probability normalization across classes
   • Each case belongs to one of the modeled mutually exclusive alternatives
5. Un clasificador tiene una precisión general alta pero una recuperación débil para una categoría poco común. ¿Qué informe ayuda a exponer esto?
   • Overall accuracy and the number of examples in each class
   • Per-class recall or precision alongside a confusion matrix
   • Only aggregate training loss across all examples
   • The total number of model coefficients
6. Why check the class-label ordering associated with probability columns?
   • Probability columns are always sorted from largest to smallest
   • Each column follows the raw input-file row order
   • Each probability column must be matched to its documented class label
   • The first column always represents the positive class
7. Which training objective is commonly used for multinomial logistic regression?
   • Squared error between arbitrary integer class IDs
   • A hinge objective on the top two scores
   • Zero-one error as a smooth differentiable training loss
   • Multinomial cross-entropy or log loss
8. A model ranks the correct class first but often assigns it 0.99 when it is right only about 0.7 of the time. What issue is present?
   • Poor calibration: confidence exceeds the observed success frequency
   • Poor recall: the predicted class is not the most frequent class
   • A violation of mutual exclusivity because confidence is high
   • A probability-sum error, even if all class probabilities sum to one