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sci.ML-regression-RMSE-R2 Calculator
Calculates regression model performance metrics: RMSE = √(Σ(y−ŷ)²/n), MAE, R² = 1 − SSres/SStot, and adjusted R². R² inflates with more predictors even if they add no information — adjusted R² penalises extra parameters and decreases if a predictor does not improve the model.
Inputs
N Samples
Count of items or occurrences.
Ss Res
Reference formula or conversion factor shown for context.
Ss Tot
Reference formula or conversion factor shown for context.
Mean Abs Err
Arithmetic average. Sensitive to outliers — if your data has extreme values, the median may be more representative.
Results
R² coefficient of determination
The value at the specified point or condition.
RMSE (root mean squared error)
The difference between the computed result and the exact or true value — a measure of approximation accuracy.
MSE (mean squared error)
The difference between the computed result and the exact or true value — a measure of approximation accuracy.
adjusted R² (n samples, 1 predictor)
Sample size or count used in the calculation.
R² = 1 − SSres/SStot
Reference formula or conversion factor shown for context.
model quality
A qualitative assessment of how the result compares to the desired standard or benchmark.