Decision Trees split data to minimize which metric in classification?

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BlackTom AI · Accepted Answer

When we talk about decision trees for classification, the goal is to split the data in a way that makes the resulting subsets as pure as possible. 
Option RMSE: Root Mean Squared Error is a regression metric that measures deviation of predicted numeric values from true values. It is not used for splitting in standard classification trees, so this option misapplies a regression criterion to a classification task. 
Option Entropy: Entropy is a measure of impurity based on the distribution of classes in a node. Splits are chosen to reduce this impurity, typically via information gain, which compares the entropy before and after a split. This directly aligns with the objective of classification trees to produce purer child nodes. 
Option SSD: Sum of Squared Deviations is another regression-oriented criterion, quantifying variability around a mean in numerical targets. It does not capture class purity and is therefore unsuitable for splitting in classification trees. 
Option Accuracy: Accuracy is a performance metric evaluated after predictions are made, not an impurity measure used to decide how to split data during tree construction. While high accuracy is desirable, it does not guide the splitting process itself. 
In summary, among the given choices, entropy is the impurity-based criterion that guides how decision trees partition data in classification tasks, whereas the other options pertain to regression metrics or post-hoc performance rather than the splitting criterion.

BA 3551 (002 & 003) Exam 2

Decision Trees split data to minimize which metric in classification?

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