Task 4

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The PerformanceVectors you provided show that the gradient boosted model outperforms the linear regression model on all metrics except for the prediction average. This suggests that the gradient boosted model is a better fit for the energy efficiency dataset. Here is a more detailed explanation of each metric: Root mean squared error (RMSE): The RMSE measures the average distance between the predicted values and the actual values. A lower RMSE indicates a better fit. Absolute error (AE): The AE measures the average difference between the predicted values and the actual values. A lower AE indicates a better fit. Normalized absolute error (NAE): The NAE is the AE divided by the range of the actual values. A lower NAE indicates a better fit. Correlation: The correlation measures the strength and direction of the linear relationship between the predicted values and the actual values. A correlation of 1 indicates a perfect positive linear relationship, a correlation of 0 indicates no linear relationship, and a correlation of -1 indicates a perfect negative linear relationship.
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