Which statement best defines misclassification rate in a predictive model?

Which statement best defines misclassification rate in a predictive model?

• A. The proportion of observations predicted incorrectly.
• B. The total number of incorrect predictions.
• C. The percentage of observations that were predicted correctly.
• D. The sum of squared residuals of prediction.

Answer: (A)

Misclassification rate tells you how often the model puts observations into the wrong category. It is a proportion: the number of misclassified observations divided by the total number of observations, usually shown as a fraction or percentage. This makes it a measure of error per case, normalized by sample size. The option describing the total number of incorrect predictions is just a raw count, not a rate, so it lacks normalization. The option about the percentage predicted correctly describes accuracy, not misclassification rate. The sum of squared residuals belongs to regression and measures how far numeric predictions are from actual values, not the rate of misclassification. So the best definition is the proportion of observations predicted incorrectly.

Misclassification rate tells you how often the model puts observations into the wrong category. It is a proportion: the number of misclassified observations divided by the total number of observations, usually shown as a fraction or percentage. This makes it a measure of error per case, normalized by sample size. The option describing the total number of incorrect predictions is just a raw count, not a rate, so it lacks normalization. The option about the percentage predicted correctly describes accuracy, not misclassification rate. The sum of squared residuals belongs to regression and measures how far numeric predictions are from actual values, not the rate of misclassification. So the best definition is the proportion of observations predicted incorrectly.