get_mse_rmse

get_mse_rmse(y_true, y_pred, *, sample_weight=None)

Compute Mean Squared Error (MSE) and Root Mean Squared Error (RMSE).

This function is a convenience wrapper that returns both Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) in a single call for streamlined regression model evaluation.

Parameters

Name	Type	Description	Default
y_true	array-like of shape (n_samples,)	True target values (e.g., list, NumPy array, or pandas Series).	required
y_pred	array-like of shape (n_samples,)	Predicted target values (same shape as y_true).	required
sample_weight	array-like of shape (n_samples,)	Sample weights (e.g., list, NumPy array, or pandas Series). If provided, errors are aggregated using a weighted mean.	None

Returns

Name	Type	Description
metrics	dict	Dictionary with: - "mse" : float Mean Squared Error computed as the mean of squared residuals. - "rmse" : float Root Mean Squared Error computed as the square root of MSE.

Notes

• MSE is defined as: mean((y_true - y_pred)**2).
• RMSE is defined as: sqrt(MSE).
• Inputs are expected to be one-dimensional (1D) and of equal length.

Raises

Name	Type	Description
	ValueError	If y_true and y_pred have different lengths, are empty, or cannot be converted into compatible numeric arrays.

Examples

>>> from reportrabbit import mse_rmse as mr
>>> y_true = [3.0, -0.5, 2.0, 7.0]
>>> y_pred = [2.5, 0.0, 2.0, 8.0]
>>> mr.get_mse_rmse(y_true, y_pred)
{'mse': 0.375, 'rmse': 0.6123724357}

Using NumPy arrays:

>>> import numpy as np
>>> y_true = np.array([1.0, 2.0, 3.0])
>>> y_pred = np.array([1.5, 1.8, 2.2])
>>> mr.get_mse_rmse(y_true, y_pred)
{'mse': 0.31, 'rmse': 0.556776436283}