AI-900: How Does Mean Squared Error (MSE) Compare Predicted vs. Actual Labels in Linear Regression?

Learn how mean squared error (MSE) is used to compare predicted labels to actual labels in linear regression for machine learning tasks. Discover why this metric is crucial for model evaluation.

Question

When using linear regression for machine learning tasks, how can you compare predicted labels to actual labels?

A. Use a regularization term.
B. Use a sigmoid function.
C. Use mean squared error (MSE) metrics.
D. Use two different learning rates for predicted and actual values.

Answer

C. Use mean squared error (MSE) metrics.

Explanation

The best way to compare predicted labels to actual labels when using linear regression for machine-learning tasks is by using mean squared error (MSE) metrics. MSE is a popular performance metric for regression models. It measures the average difference between the values predicted by the model and the actual values, squared. A lower MSE indicates a better fit, meaning that the model’s predictions are closer to the true values. Some commonly used metrics for evaluating regression models are:

1. Mean Absolute Error (MAE): This measures the average magnitude of the errors, regardless of direction (overestimation or underestimation).
2. MSE: This is the average of the squared differences between predicted and actual values, penalizing larger errors more heavily.
3. Root Mean Squared Error (RMSE): This is like MSE but is expressed in the same units as the target variable, making interpretation easier.
4. Coefficient of Determination (R-squared): This is the proportion of variance in the target variable explained by the model.

A sigmoid function is not used to compare predicted labels to actual labels. Sigmoid functions are typically used in logistic regression models which are designed for classification tasks involving discrete labels (e.g., spam or not spam). Linear regression deals with predicting continuous numerical values and using a sigmoid function would not provide a meaningful comparison metric.

A regularization term is not used to compare predicted labels to actual labels. Regularization techniques primarily address the model’s complexity and generalization ability. They can improve the performance of linear regression models by preventing overfitting.

Using two different learning rates for predicted and actual values does not compare predicted labels to actual labels. This approach does not make sense in the context of comparing predicted and actual labels. Learning rates control the step size during the model training process, not the evaluation of predictions.

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