Which evaluation metric is best suited for regression problems?
Practice Questions
Q1
Which evaluation metric is best suited for regression problems?
Accuracy
F1 Score
Mean Absolute Error
Precision
Questions & Step-by-Step Solutions
Which evaluation metric is best suited for regression problems?
Step 1: Understand what regression problems are. These are problems where we predict a continuous value, like predicting house prices or temperatures.
Step 2: Learn about evaluation metrics. These are ways to measure how well our predictions match the actual values.
Step 3: Identify common evaluation metrics for regression. Some examples include Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).
Step 4: Focus on Mean Absolute Error (MAE). This metric calculates the average of the absolute differences between predicted values and actual values.
Step 5: Understand why MAE is useful. It gives a clear idea of how far off our predictions are, without squaring the errors, which can exaggerate larger errors.
