Explanation:
AutoML can use a variety of evaluation metrics for regression problems to assess the performance of a model. Some common metrics include mean squared error (MSE), which measures the average squared difference between the predicted and actual values; mean absolute error (MAE), which measures the average absolute difference between the predicted and actual values; and R-squared, which measures the proportion of the variance in the dependent variable that is predictable from the independent variables. Option B correctly identifies the evaluation metrics that AutoML can use for regression problems and their significance in evaluating regression models.
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In the context of regression problems, explain the evaluation metrics that AutoML can use to assess the performance of a model. Provide examples of these metrics and their significance in evaluating regression models.
A
AutoML can only use the mean squared error (MSE) metric for evaluating regression models.
B
AutoML can use a variety of evaluation metrics for regression problems, such as mean squared error (MSE), mean absolute error (MAE), and R-squared.
C
AutoML can only use the R-squared metric for evaluating regression models.
D
AutoML cannot use any evaluation metrics for regression problems, as it relies solely on the model's predictions to assess performance.
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