Global and local feature (predictor variable) explanations from the XGBoost trained with Quebec data (2014–2021).
<p>Left: bar chart showing the global importance of each feature, measured as the mean absolute SHAP value across all observations. Higher values indicate greater overall influence on the model’s predictions. Right: The local explanation summary plot indicates how each feature observation cont...
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2025
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| Summary: | <p>Left: bar chart showing the global importance of each feature, measured as the mean absolute SHAP value across all observations. Higher values indicate greater overall influence on the model’s predictions. Right: The local explanation summary plot indicates how each feature observation contributes to the model’s predictions. Each dot represents a site, with colour indicating the feature value (red = high, blue = low). Dot position along the x-axis is the SHAP value, showing how much that feature shifts the model’s prediction from the baseline on a log-odds scale, with positive values increasing the prediction and negative values decreasing the prediction. The baseline prediction (the model’s average output) was a log-odds of approximately −0.212, corresponding to a probability of about 0.44. For a single feature, predicted log-odds for a site is calculated by adding that feature’s SHAP value to the baseline. For example, a high DD > 0°C value contributing a SHAP value of +2.2 would increase the predicted probability from the baseline of 0.44 to 0.88 as follows: log-odds = Baseline + SHAP_DD > 0 = −0.212 + 2.2 = 1.988 and the final probability, p, would be p = 1/ (1 + e^(−1.988)) ≈ 0.88.</p> |
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