Decision Trees in Depth

Five questions. Choose one answer for each, then submit to see your score and explanations.

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1. Which explanation matches measure impurity in a split?A. Information gain compares parent impurity with the weighted impurity after a split.B. Pruning removes weak branches or limits tree growth to reduce overfitting.C. Impurity measures how mixed target labels are within a candidate group.D. A decision path lists the conditions traversed for that particular prediction.

2. Which explanation matches choose a feature with information gain?A. Impurity measures how mixed target labels are within a candidate group.B. Information gain compares parent impurity with the weighted impurity after a split.C. Pruning removes weak branches or limits tree growth to reduce overfitting.D. A decision path lists the conditions traversed for that particular prediction.

3. Which explanation matches prune a tree before it memorizes noise?A. Pruning removes weak branches or limits tree growth to reduce overfitting.B. Impurity measures how mixed target labels are within a candidate group.C. Information gain compares parent impurity with the weighted impurity after a split.D. A decision path lists the conditions traversed for that particular prediction.

4. For choose a feature with information gain, which result or action fits this practical case: Distance or weather could be the tree's first question.A. Compare a node with all on-time deliveries to one split evenly between late and on-time.B. Choose depth on validation data, then compare train and validation errors.C. Show the route, weather, and time splits that led to one leaf.D. Compute each candidate split from training labels and prefer the useful reduction.

5. For explain a path without overclaiming, which result or action fits this practical case: A dispatcher asks why one delivery was flagged.A. Compare a node with all on-time deliveries to one split evenly between late and on-time.B. Compute each candidate split from training labels and prefer the useful reduction.C. Show the route, weather, and time splits that led to one leaf.D. Choose depth on validation data, then compare train and validation errors.

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Decision Trees in Depth

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