Calculate the RMSE between predicted and observed values. Input your values and visualize the differences between them.
Root Mean Squared Error (RMSE):
Understanding Root Mean Squared Error (RMSE)
The Root Mean Squared Error (RMSE) is a standard way to measure the error of a model in predicting quantitative data. It represents the square root of the average squared differences between predicted values \( P_i \) and observed values \( O_i \).
Formula for RMSE
Example Calculation
Let's calculate the RMSE for the following observed and predicted values:
- Observed values: [34, 37, 44, 47, 48]
- Predicted values: [37, 40, 46, 44, 46]
Following the formula:
- Find the differences between each pair of predicted and observed values:
- Difference 1: \( 37 - 34 = 3 \)
- Difference 2: \( 40 - 37 = 3 \)
- Difference 3: \( 46 - 44 = 2 \)
- Difference 4: \( 44 - 47 = -3 \)
- Difference 5: \( 46 - 48 = -2 \)
- Square each difference:
- \( 3^2 = 9 \)
- \( 3^2 = 9 \)
- \( 2^2 = 4 \)
- \( (-3)^2 = 9 \)
- \( (-2)^2 = 4 \)
- Sum the squared differences: \( 9 + 9 + 4 + 9 + 4 = 35 \)
- Divide by the number of observations: \( 35 / 5 = 7 \)
- Take the square root: \( \sqrt{7} \approx 2.646 \)
Therefore, the RMSE for this example is approximately 2.646.
Further Reading
Suf is a senior advisor in data science with deep expertise in Natural Language Processing, Complex Networks, and Anomaly Detection. Formerly a postdoctoral research fellow, he applied advanced physics techniques to tackle real-world, data-heavy industry challenges. Before that, he was a particle physicist at the ATLAS Experiment of the Large Hadron Collider. Now, he’s focused on bringing more fun and curiosity to the world of science and research online.