Average Error Calculator
Calculate mean absolute error (MAE), root mean square error (RMSE), and other error metrics for your data analysis
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Error Metrics to Calculate
Error Metrics Results
Enter your data to see interpretation of your error metrics.
Detailed Error Analysis
Your detailed error analysis will appear here.
Error Visualization
A visualization of actual vs predicted values will appear here after calculation.
📊 Common Error Benchmarks
| Industry | Good MAE | Good RMSE | Typical Range |
|---|---|---|---|
| Retail Forecasting | < 10% of mean | < 15% of mean | 5-20% of mean |
| Financial Models | < 5% | < 7% | 3-10% |
| Weather Prediction | 1-2°C | 1.5-3°C | Varies by model |
| Machine Learning | Dependent on scale | Dependent on scale | Compare to baseline |
| Economic Forecasts | < 1% for GDP | < 1.5% for GDP | 0.5-2% |
Note: Benchmarks vary by application and data scale. Always compare to a naive baseline.
📚 Reducing Prediction Error
Feature Engineering
Create better input features that have stronger relationships with your target variable.
Model Selection
Try different algorithms - some models work better for certain types of data patterns.
Data Quality
Clean your data by handling missing values, outliers, and inconsistencies.
Cross-Validation
Use k-fold cross-validation to ensure your model generalizes well to unseen data.
Hyperparameter Tuning
Optimize your model parameters using grid search or random search techniques.
Ensemble Methods
Combine multiple models using bagging or boosting to improve accuracy.
Note: This calculator provides common error metrics for evaluating prediction accuracy. The interpretation of "good" error values depends on your specific domain and application.