What it is:
- Average of all regression coefficients across 100 runs
- Includes the intercept (bias term) as the first value, followed by coefficients for each feature
What it signifies:
- Each coefficient shows the expected change in target (house price) per unit increase of that feature
- Positive coefficient → increasing feature increases predicted price
- Negative coefficient → increasing feature decreases predicted price
- Averaging over 100 runs reduces randomness and provides a stable estimate of feature importance
What it is:
- Standard deviation of each coefficient across the 100 runs
What it signifies:
- Measures how much each coefficient varies due to random train/test splits
- Small std → coefficient is stable across splits → feature is reliably important
- Large std → coefficient is unstable → feature's effect may be inconsistent
What it is:
- Average of all absolute differences between predicted and actual house prices across all runs
What it signifies:
- Represents the typical size of prediction error in the model
- Example:
mean_error = 3.2→ on average, predicted price is off by 3.2 units (e.g., $3,200 if target is in thousands)
What it is:
- Standard deviation of absolute prediction errors
What it signifies:
- Measures variability of prediction error across predictions
- Small std → predictions are consistently close to actual values
- Large std → some predictions are much worse than others
What it is:
- Root Mean Squared Error averaged over all 100 runs
What it signifies:
- Provides a penalized measure of prediction error (larger mistakes are weighted more heavily)
- Commonly used in regression to report model accuracy
What it is:
- Standard deviation of RMSE across all runs
What it signifies:
- Shows how much model performance varies with different random train/test splits
- Smaller RMSE std → model's accuracy is consistent
What it is:
- A range containing 95% of the absolute prediction errors
- Example:
(1.0, 7.5)→ 95% of predicted prices are within 1.0–7.5 units of the true price
What it signifies:
- Provides a statistical range of likely prediction errors
- Makes model performance trustworthy and interpretable
- Can be reported as: "The model predicts house prices with 95% of errors falling between $1,000 and $7,500."
Use these statistics to describe your model in a trustworthy way:
| Metric | Definition |
|---|---|
| Feature Importance | mean_beta ± std_beta |
| Prediction Accuracy | mean_error ± std_error |
| Typical Error Magnitude | rmse_mean ± rmse_std |
| Reliability of Predictions | 95% prediction interval |