Regression
Mean squared error
Implemented
regression.mseDefinition
Average squared difference between predictions and targets.
Formula
MSE = (1/N) Σᵢ (yᵢ − ŷᵢ)²
Range: [0, ∞)
Inputs and outputs
- y_true: array of real targets
- y_pred: array of real predictions
- sample_weight: optional
Returns: float
Assumptions
No assumptions beyond valid, aligned inputs of the documented types.
Limitations
- Sensitive to outliers because errors are squared.
Python API
import evalsuite as es
es.mse(y_true_r, y_pred_r)References
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