Regression metric computation rmse mae r2

regression-metric-computation-rmse-mae-r2

Summary

Compute root-mean-square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²) to quantify the generalizability and predictive accuracy of regression models on held-out test data. This skill is essential for validating graph neural network models trained for molecular property prediction across independent datasets.

When to use

Apply this skill when you have model predictions and ground-truth labels for a test set and need to assess how well the trained model generalizes to unseen data. Typical triggers: after executing inference on a held-out test partition, after cross-dataset evaluation (e.g., training on METLIN-CCS and testing on CCSBase), or when comparing baseline models against new approaches.

When NOT to use

• Input is classification labels (discrete/categorical) rather than continuous numeric predictions — use classification metrics (accuracy, precision, recall, F1, AUC) instead.
• Predictions or labels contain missing values (NaN/inf) — impute, filter, or handle missing data before metric computation.
• Test set is extremely small (n < 10) or highly imbalanced with outliers — RMSE and MAE may be dominated by a few extreme errors; consider robust alternatives (median absolute error, quantile loss) or log-transform the target.

Inputs

• predicted values (numeric array or column from model inference)
• ground-truth test labels (numeric array or column, same shape as predictions)
• baseline metric values (for reproducibility comparison)

Outputs

• RMSE (scalar numeric value, same units as target variable)
• MAE (scalar numeric value, same units as target variable)
• R² (scalar numeric value, typically 0–1 range, 1 is perfect prediction)
• aggregated results table with metrics alongside reported baselines

How to apply

After loading predictions and ground-truth test labels, compute RMSE as the square root of mean squared differences, MAE as the mean of absolute differences, and R² as the proportion of variance explained by the model. These metrics should be computed on the full test set without filtering or subsetting. The workflow: (1) align predicted and ground-truth values by index; (2) compute element-wise differences; (3) apply the metric formulas (RMSE = sqrt(mean((y_pred - y_true)²)), MAE = mean(|y_pred - y_true|), R² = 1 - SS_res / SS_tot); (4) report each metric as a scalar value; (5) compare results against baseline values reported in the paper to verify reproducibility. These metrics are model-agnostic and work for any continuous-valued prediction task.

Related tools

• scikit-learn metrics module (Provides mean_squared_error, mean_absolute_error, r2_score functions for direct metric computation)
• PyTorch Geometric (Graph neural network framework used to train the CCS prediction model whose outputs are evaluated by these metrics)
• enveda/ccs-prediction (Repository containing pre-trained GNN checkpoints, test data, and scripts to generate predictions on which metrics are computed) — https://github.com/enveda/ccs-prediction

Examples

from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score; rmse = sqrt(mean_squared_error(y_true, y_pred)); mae = mean_absolute_error(y_true, y_pred); r2 = r2_score(y_true, y_pred); print(f'RMSE: {rmse}, MAE: {mae}, R²: {r2}')

Evaluation signals

• RMSE value is strictly non-negative (≥ 0) and has the same physical units as the target (e.g., collision cross section in Ų).
• MAE value is strictly non-negative (≥ 0), ≤ RMSE (MAE ≤ RMSE by Cauchy–Schwarz inequality), and expressed in target units.
• R² value lies in [−∞, 1]; R² = 1 indicates perfect predictions; R² = 0 indicates the model explains no more variance than predicting the mean; R² < 0 indicates the model performs worse than a constant baseline.
• Reported metrics match (or closely reproduce, within numerical precision) the baseline values listed in the paper for the same train–test split combination.
• Metric values are computed only on the designated test set partition (not train or validation) to avoid overfitting bias.

Limitations

• RMSE is sensitive to outliers; a single large prediction error disproportionately increases RMSE. If the test set contains extreme outliers, report MAE alongside RMSE or use robust loss metrics.
• R² is undefined or misleading when SS_tot = 0 (all ground-truth labels are identical); always verify that test labels have non-zero variance before interpreting R².
• These metrics assume predictions are on the same scale as ground-truth labels; if predictions were made on log-transformed or scaled data, back-transform before metric computation to ensure interpretability.
• Cross-dataset evaluation (e.g., training on METLIN-CCS and testing on CCSBase) may show large metric gaps due to domain shift or instrumental differences, not model failure; metrics alone do not diagnose the root cause.

Evidence

• [other] Compute generalizability metrics (RMSE, MAE, R²) comparing predictions against ground-truth test labels.: "Compute generalizability metrics (RMSE, MAE, R²) comparing predictions against ground-truth test labels."
• [other] Execute model inference on the test set using the trained GNN to generate collision cross section predictions.: "Execute model inference on the test set using the trained GNN to generate collision cross section predictions."
• [other] Aggregate results and tabulate metric values alongside reported baseline values for reproducibility verification.: "Aggregate results and tabulate metric values alongside reported baseline values for reproducibility verification."
• [readme] Run the notebooks located in the notebooks corresponding to each analysis.: "Run the notebooks located in the notebooks corresponding to each analysis."