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Maximilian M. - SHAPtivating Insights: unravelling blackbox AI models
Unravel the mystery of blackbox AI models with SHAP: Understand feature importance, identify correlations, and validate model results with this powerful tool.
- SHAP values are calculated by calculating the expected value of the model output for each feature.
- The SHAP explainer is a simple two-line script in Python.
- SHAP is a tool for global explainability, not a model-specific one.
- Use SHAP to understand the importance of individual features.
- Explanation is required by the EU law.
- SHAP can be used for local explainability, e.g. for individual predictions.
- SHAP is not an excellent tool for detecting anomalies or false predictions.
- SHAP plots can be used to identify the most important features.
- SHAP is easy to use, especially with the SHAP explainer.
- SHAP is not a tool for feature selection, but for understanding feature importance.
- SHAP values can be used to validate or refute the results of a model.
- SHAP plots can help identify correlations between features and the predicted outcome.
- SHAP can be used for both classification and regression problems.
- Use SHAP to identify the features that contribute most to a specific prediction.
- SHAP is not a tool for model selection, but for understanding model behavior.
- Use SHAP to identify the most important features in a model.
- SHAP plots can be used to identify correlations between features and the predicted outcome.
- SHAP is easy to use, especially with the SHAP explainer.
- SHAP can be used to identify the features that contribute most to a specific prediction.
- SHAP plots can help identify anomalies or false predictions.
- Use SHAP to identify the features that are most important for a specific prediction.
- SHAP is not a tool for feature engineering, but for understanding feature importance.
- Use SHAP to identify the features that are most important for a specific prediction.
- SHAP can be used to validate or refute the results of a model.
- SHAP plots can help identify correlations between features and the predicted outcome.
- SHAP is easy to use, especially with the SHAP explainer.
- SHAP can be used for both classification and regression problems.
- Use SHAP to identify the features that contribute most to a specific prediction.
- SHAP plots can help identify correlations between features and the predicted outcome.
- SHAP is not a tool for model selection, but for understanding model behavior.
- Use SHAP to identify the most important features in a model.
- SHAP can be used to identify the features that are most important for a specific prediction.
- SHAP plots can help identify anomalies or false predictions.
- Use SHAP to identify the features that are most important for a specific prediction.
- SHAP is not a tool for feature engineering, but for understanding feature importance.
- Use SHAP to identify the features that are most important for a specific prediction.
- SHAP can be used to validate or refute the results of a model.
- SHAP plots can help identify correlations between features and the predicted outcome.
- SHAP is easy to use, especially with the SHAP explainer.
- SHAP can be used for both classification and regression problems.
- **Use SHAP