Prueba de impureza de Gini

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

1. A binary node has class proportions 0.8 and 0.2. What is its Gini impurity?
   • 0.32
   • 0.20
   • 0.64
   • 0.80
2. Which value describes impurity in a node containing only one class?
   • 0,5
   • 0
   • 1
   • It depends on the number of predictors
3. How should child impurities be combined when evaluating a candidate split?
   • Add child scores without considering child sizes
   • Take the largest child impurity only
   • Weight each child by its share of the parent observations
   • Use only the purity of the smallest child
4. What does a Gini decrease represent in tree construction?
   • The probability that a feature is causal
   • The fraction of training labels predicted incorrectly on test data
   • The number of classes in the target
   • Parent impurity minus weighted child impurity
5. For two equally common classes in a binary node, what is Gini impurity?
   • 0
   • 0,5
   • 0.25
   • 1
6. Which distinction between Gini and entropy is accurate?
   • Entropy is a distance between predictor vectors
   • Gini evaluates held-out accuracy while entropy builds trees
   • They are different local impurity criteria with different scales
   • Gini is always numerically larger for every split
7. A deep tree has nearly pure training leaves. What should be checked before concluding it generalizes well?
   • Whether every feature has a positive value
   • Whether every leaf has zero entropy only
   • The alphabetical order of class labels
   • Performance on appropriately held-out data
8. What does node-local Gini impurity describe?
   • Class mixing among observations in that node
   • The global class balance in every dataset
   • The probability the fitted model is calibrated
   • Feature importance across all possible models