You are comparing two machine learning models built to predict continuous values—specifically, estimating commercial property values based on features like square footage, location, and age. Model 1 is a linear regression model, and Model 2 is a random forest regressor. Which two statistical metrics are most appropriate for evaluating the prediction accuracy and explanatory power of these regression models? (Select two)
Select all correct answers, then click Submit.
Short Explanation
Here's the deal: you're predicting property values, which are continuous numbers, so you need regression metrics. Use Mean Absolute Error to see the average dollar error and R-squared to see how much variance your model explains. Don't get tripped up by classification metrics or training knobs.
Full Explanation
Evaluating machine learning models requires selecting metrics that match the task's mathematical nature. Predicting commercial property values is a regression task because the target variable is continuous (numeric), rather than categorical (discrete).
Mean Absolute Error (MAE) measures the average magnitude of the absolute errors between the model's predictions and the actual values. It is calculated as the average of the absolute differences across all test samples. Because it is expressed in the same units as the target variable (e.g., dollars), it provides an intuitive measure of prediction accuracy.
R-squared (Coefficient of Determination) measures the proportion of variance in the dependent variable that is predictable from the independent variables. Ranging from 0 to 1 (or negative in poor models), it indicates the goodness-of-fit and reliability of the model's explanatory power.
Analyzing the incorrect options: - The F1 Score is the harmonic mean of precision and recall. It is used exclusively in binary or multi-class classification tasks to evaluate the balance between false positives and false negatives. - Cross-Entropy Loss measures the performance of a classification model whose output is a probability value between 0 and 1, quantifying the difference between two probability distributions. - Learning Rate is a training hyperparameter that controls the step size at each iteration while moving toward a minimum of a loss function, not a performance metric for evaluating a trained model.
Therefore, Mean Absolute Error and R-squared are the correct metrics for assessing regression performance. Confusing these on the exam is a classic trap.