By changing the model assessment measure to which option can you construct a class probability tree?

By changing the model assessment measure to which option can you construct a class probability tree?

• A. Average Square Error
• B. Misclassification
• C. Root Mean Square Error
• D. Likelihood Ratio

Answer: (A)

To construct a class probability tree in SAS Enterprise Miner, the most suitable model assessment measure is based on evaluating the model's performance in terms of class probabilities, which is critical for understanding and interpreting classification outcomes. The average square error is a measure typically used in regression analysis to assess the average of the squares of errors or deviations of predictions from actual values. It focuses on overall prediction accuracy without specifically addressing class membership probability distributions that are central to constructing a class probability tree. On the other hand, misclassification as an assessment measure quantifies the number of incorrect predictions made by the model. While this metric is useful for evaluating classification performance, it does not provide a probabilistic framework, which is necessary for building a tree structure representing class probabilities. Root mean square error, similar to average square error, assesses error magnitude but is again oriented towards overall prediction errors rather than the probabilistic aspects of class assignments. The likelihood ratio is a statistical measure that compares the likelihoods of two competing hypotheses. While it can provide insights into model fit and predictions, it does not directly relate to the practical construction of a class probability tree in the context of model assessment. Therefore, focusing on average square error does not align with the objective of creating a class probability tree, which

To construct a class probability tree in SAS Enterprise Miner, the most suitable model assessment measure is based on evaluating the model's performance in terms of class probabilities, which is critical for understanding and interpreting classification outcomes.

The average square error is a measure typically used in regression analysis to assess the average of the squares of errors or deviations of predictions from actual values. It focuses on overall prediction accuracy without specifically addressing class membership probability distributions that are central to constructing a class probability tree.

On the other hand, misclassification as an assessment measure quantifies the number of incorrect predictions made by the model. While this metric is useful for evaluating classification performance, it does not provide a probabilistic framework, which is necessary for building a tree structure representing class probabilities.

Root mean square error, similar to average square error, assesses error magnitude but is again oriented towards overall prediction errors rather than the probabilistic aspects of class assignments.

The likelihood ratio is a statistical measure that compares the likelihoods of two competing hypotheses. While it can provide insights into model fit and predictions, it does not directly relate to the practical construction of a class probability tree in the context of model assessment.

Therefore, focusing on average square error does not align with the objective of creating a class probability tree, which