Analysis Report¶

Global dataset report¶

This report is the output of the Amazon SageMaker Clarify analysis. The report is split into following parts:

    1. Model explanations

Model explanations¶

SHAP Explanations for individual labels¶

The model has 30 features.

We computed KernelSHAP explanations on the dataset. For each label, we display the 10 features with the greatest feature attribution. You have 1 label(s).

In the chart below, each point in the plot denotes an individual instance being explained. x-axis shows the SHAP value for the corresponding instance and feature. The red-blue color scale shows the value of the feature itself. Red indicates higher values whereas blue indicates lower values.

Appendix: Analysis Configuration Parameters¶

{
    "dataset_type": "text/csv",
    "headers": [
        "diagnosis",
        "radius_mean",
        "texture_mean",
        "perimeter_mean",
        "area_mean",
        "smoothness_mean",
        "compactness_mean",
        "concavity_mean",
        "concave points_mean",
        "symmetry_mean",
        "fractal_dimension_mean",
        "radius_se",
        "texture_se",
        "perimeter_se",
        "area_se",
        "smoothness_se",
        "compactness_se",
        "concavity_se",
        "concave points_se",
        "symmetry_se",
        "fractal_dimension_se",
        "radius_worst",
        "texture_worst",
        "perimeter_worst",
        "area_worst",
        "smoothness_worst",
        "compactness_worst",
        "concavity_worst",
        "concave points_worst",
        "symmetry_worst",
        "fractal_dimension_worst"
    ],
    "label": "diagnosis",
    "predictor": {
        "model_name": "xgboost-breast-cancer-model-1736643545-model",
        "instance_type": "ml.m5.large",
        "initial_instance_count": 1,
        "accept_type": "text/csv",
        "content_type": "text/csv"
    },
    "methods": {
        "report": {
            "name": "report",
            "title": "Analysis Report"
        },
        "shap": {
            "use_logit": false,
            "save_local_shap_values": true,
            "baseline": [
                [
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5,
                    0.5
                ]
            ],
            "num_samples": 100,
            "agg_method": "mean_abs"
        }
    }
}