Error Cuadrático Medio (MSE) is a statistical metric used to evaluate the accuracy of a model’s predictions by quantifying the difference between predicted values and the actual values observed in the data.
La fórmula para calcular el MSE es:
MSE = (1/n) * Σ(actual – predicted)²
Aquí, n is the number of observations, actual represents the true values, and predicted are the values generated by the model. The squared differences are used to ensure that positive and negative errors do not cancel each other out, emphasizing larger errors more than smaller ones.
El MSE se usa ampliamente en análisis de regresión and aprendizaje automático to assess how well a model performs. A lower MSE value indicates better rendimiento del modelo, as it signifies that the predictions are closer to the actual values. Conversely, a higher MSE indicates larger errors and poorer model accuracy.
While MSE is a useful metric, it is important to note that it is sensitive to outliers due to the squaring of errors. Therefore, in cases where the data may contain outliers, other metrics like Error Absoluto Medio (MAE) podría considerarse para la evaluación.
En resumen, el Error Cuadrático Medio es un concepto fundamental en modelado predictivo, providing a clear numeric value that reflects the quality of a model’s predictions.