loss_function_rmse

Loss function: Root Mean Square Error.

rmse = loss_function_rmse(y_true, y_pred)

Input variables

Name	Description	Type
y_true	True values	List
y_pred	Predicted values	List

Output variables

Name	Description	Type
rmse	Root Mean Square Error	Float

Problem

$$f({\mathbf{y}}_{\text{true}},{\mathbf{y}}_{\text{pred}})=\sqrt{\frac{1}{n}\cdot \sum _{i=1}^n(y_{\text{true},i}-y_{\text{pred},i}{)}^2}$$

(1)

$n$ is the number of samples.

Example 1

Considering the true values ${\mathbf{y}}_{\text{true}}=[1.0,2.0,3.0,4.0,5.0]$ and predicted values ${\mathbf{y}}_{\text{pred}}=[1.2,2.3,2.9,4.2,5.3]$, what is the resulting Root Mean Square Error (RMSE)?

# Data
y_true_example = [1.0, 2.0, 3.0, 4.0, 5.0]
y_pred_example = [1.2, 2.3, 2.9, 4.2, 5.3]

# Call function
rmse_value = loss_function_rmse(y_true_example, y_pred_example)

# Print the result
print("Root Mean Square Error (RMSE): {:.4f}".format(rmse_value))

Root Mean Square Error (RMSE): 0.2324