Root Mean Square Error In Machine Learning

Root Mean Square Error In Machine Learning

 

Root Mean Square Error (RMSE) is a widely used performance metric for evaluating regression models in Machine Learning. It measures the square root of the average of the squared differences between predicted values and actual observations. RMSE provides an estimate of the model’s prediction error in the same units as the target variable, making it highly interpretable. Because it penalizes larger errors more heavily than smaller ones, RMSE is sensitive to outliers and is particularly useful when large deviations are undesirable. It is commonly applied in forecasting, financial modeling, healthcare analytics, and scientific research.

Root Mean Square Error, RMSE, Regression Metrics, Model Evaluation, Prediction Error, Mean Squared Error (MSE), Error Measurement, Forecasting Accuracy, Loss Function, Model Validation, Supervised Learning, Predictive Modeling, Residual Analysis, Performance Evaluation, Statistical Metrics, Accuracy Assessment.

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