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Decoding Data’s True Score
- Regression metrics are crucial for forecasting accurate outcomes in various industries like retail, real estate, tech, and agriculture.
- MAE measures the average difference between model predictions and actual values, helping to understand typical deviations.
- MSE calculates the average squared difference between predicted and actual values, penalizing larger errors.
- RMSE, the square root of MSE, provides the average error in the original units, aiding in assessing accuracy.
- R² reveals the percentage of variance explained by the model, signifying its explanatory power.
- MAPE expresses prediction errors as a percentage of actual values, aiding in understanding forecast reliability.
- WAPE weighs errors by the size of actual values for more robust evaluation in datasets with varying scales.
- NRMSE normalizes RMSE for comparison across datasets with different scales, ensuring fair evaluation.
- MSLE penalizes underestimates more than overestimates, beneficial when target values vary significantly.
- Choosing the right regression metric is essential for data science success and translating insights into business value.
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