SHAP Values
SHAP values provide a unified measure of feature importance in machine learning models. Understand the impact of each feature on predictions.
SHAP Values Demonstration
Sample Data
| Feature 1 | Feature 2 | Feature 3 | Target |
|---|---|---|---|
| 1.5 | 2.0 | 3.5 | 1.0 |
| 2.0 | 1.0 | 4.0 | 0.5 |
Model Prediction
Assuming a simple model: target = 2 * feature 1 - feature 2 + 0.5 * feature 3
SHAP Values
| Feature | SHAP Value |
|---|---|
| Feature 1 | 0.75 |
| Feature 2 | -1.0 |
| Feature 3 | 0.5 |
Interpretation
The SHAP values indicate how much each feature contributes to the model's prediction. In this example, Feature 1 has a positive impact on the target, Feature 2 has a negative impact, and Feature 3 has a neutral impact.
```SHAP values for a sample dataset with three features and a target variable. We assume a linear model with specific coefficients to demonstrate how SHAP values are calculated and interpreted. - The "Sample Data" section presents the input data with three features and the corresponding target values. - The "Model Prediction" section describes the simple linear model used for prediction. - The "SHAP Values" section displays the SHAP values calculated for each feature based on the model's output. - The "Interpretation" section provides an explanation of how to interpret the SHAP values in the context of feature importance. You can further enhance the presentation by incorporating visualizations such as bar charts or heatmaps to illustrate the SHAP values and their impact on the model predictions. Additionally, you can customize the styling and layout of the HTML elements to make the demonstration more visually appealing and interactive. By using HTML format to display SHAP values, you can easily share and communicate the insights derived from the model interpretation process with stakeholders, decision-makers, or anyone interested in understanding the inner workings of machine learning models.
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