BenchMark Quizzes by MDFT Pro

Select Appropriate Classification Model Evaluation Metric

Mark, the Model Performance Evaluation Specialist at MDFT Pro, is establishing standardized metrics for assessing the effectiveness of classification models used throughout the company’s educational analytics systems. MDFT Pro, a well-known training agency, employs various classification models to predict student outcomes, categorize course difficulty levels, and classify student learning styles. Mark needs to select appropriate evaluation metrics that can accurately measure how well these models perform in correctly assigning students, courses, and educational content to their proper categories, ensuring the models provide reliable predictions for educational decision-making.

Which evaluation metric is appropriate for measuring the performance of classification models used in educational analytics?

Choose the correct answer from the options below.

Explanations for each answer:

True positive rate is correct. True positive rate (sensitivity/recall) measures the proportion of actual positives correctly identified, making it a key metric for evaluating classification model performance.
Mean absolute error (MAE) is incorrect. MAE measures average prediction errors for continuous values, making it appropriate for regression models, not classification models.
Coefficient of determination (R2) is incorrect. R2 measures how well a regression model explains variance in continuous target variables, not classification accuracy for discrete categories.
Root mean squared error (RMSE) is incorrect. RMSE measures prediction accuracy for continuous numeric values in regression models, not classification performance for categorical predictions.

Learn more about classification metrics:

Classification Metrics

Exam Preparation Quiz

Select Appropriate Classification Model Evaluation Metric