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Feb 07, 2021 · Looking at some of the official examples here and here I notice the plots also showcase the value of the features. The shap package contains both shap.waterfall_plot and shap.plots.waterfall, trying both on a Random Forest trained on the Iris dataset gave the same results (see one code and image example below)

Documentation by example for shap.plots.waterfall ¶. This notebook is designed to demonstrate (and so document) how to use the shap.plots.waterfall function. It uses an XGBoost model trained on the classic UCI adult income dataset (which is classification task to predict if people made over \$50k in the 90s).

07.02.2021 · Looking at some of the official examples here and here I notice the plots also showcase the value of the features. The shap package contains both shap.waterfall_plot and shap.plots.waterfall, trying both on a Random Forest trained on the Iris dataset gave the same results (see one code and image example below)

waterfall plot This notebook is designed to demonstrate (and so document) how to use the shap.plots.waterfall function. It uses an XGBoost model trained on the classic UCI adult income dataset (which is classification task to predict if people made over \$50k in the 90s). [1]:

How to create and interpret SHAP plots: waterfall, force, decision, mean SHAP, and beeswarm ... For a given prediction, SHAP values can tell us how much each ...

Found it! the shap.Explanation method has an argument where you can pass the data. See edited example below for which_class in y.unique(): ...

25.12.2021 · These SHAP values of all input features will always be summed up to the difference between the expected output from the model and that is how the output from the current model for the prediction becomes explained. We can see it through the waterfall plot. SHAP.plots.waterfall (SHAP_values [sample_ind]) Output:

waterfall_plot - It shows a waterfall plot explaining a particular prediction of the model based on shap values. It kind of shows the path of how shap values were added to the base value to come to a particular prediction. text_plot - It plots an explanation of text samples coloring text based on their shap values.

waterfall plot . This notebook is designed to demonstrate (and so document) how to use the shap.plots.waterfall function. It uses an XGBoost model trained on the classic UCI adult income dataset (which is classification task to predict if people made over \$50k in the 90s).

Is there any change in the WaterFall plot? Previously this was the syntax: shap.waterfall_plot(expected_values, shap_values[row_index], ...

The SHAP waterfall plots aims to explain how individual claim predictions are derived. ... Each intermediate value shows the impact of that ...

Waterfall plots are designed to display explanations for individual predictions, so they expect a single row of an Explanation object as input. The bottom of a ...

shap.plots.waterfall(shap_values, max_display=10, show=True) . Plots an explantion of a single prediction as a waterfall plot. The SHAP value of a feature represents the impact of the evidence provided by that feature on the model’s output. The waterfall plot is designed to visually display how the SHAP values (evidence) of each feature move ...

12.07.2021 · SHAP waterfall plot The waterfall plot is another local analysis plot of a single instance prediction. Let’s take instance number 8 as an example: row = 8 shap.waterfall_plot (shap.Explanation (values=shap_values [0] [row], base_values=explainer.expected_value [0], data=X_test.iloc [row], feature_names=X_test.columns.tolist ())) Image by Author

A waterfall plot powerfully shows why a case receives its prediction given its variable values. You start with the bottom of a waterfall plot ...

shap.plots.waterfall(shap_values[0], max_display=20) It is interesting that having a capital gain of \$2,174 dramatically reduces this person’s predicted probability of making over \$50k annually. Since waterfall plots only show a single sample worth of data, we can’t see the impact of changing capital gain.

Dec 25, 2021 · Waterfall Plot. These SHAP values of all input features will always be summed up to the difference between the expected output from the model and that is how the output from the current model for the prediction becomes explained. We can see it through the waterfall plot. SHAP.plots.waterfall(SHAP_values[sample_ind]) Output: