Front page example (XGBoost)

In [1]:

    

import xgboost
import shap

# train XGBoost model
X,y = shap.datasets.boston()
model = xgboost.XGBRegressor().fit(X, y)

# explain the model's predictions using SHAP values
# (same syntax works for LightGBM, CatBoost, and scikit-learn models)
background = shap.maskers.TabularIndependent(X, sample=100)
explainer = shap.Explainer(model, background)
shap_values = explainer(X)

# visualize the first prediction's explanation
shap.plots.waterfall(shap_values[0])


    

In [2]:

    

# plot the global importance of each feature
shap.plots.bar(shap_values[0], show=False)


    

In [3]:

    

shap.plots.initjs()

# visualize the first prediction's explanation
shap.plots.force(shap_values[0])


    

    












    
Out[3]:







  Visualization omitted, Javascript library not loaded!

  Have you run `initjs()` in this notebook? If this notebook was from another
  user you must also trust this notebook (File -> Trust notebook). If you are viewing
  this notebook on github the Javascript has been stripped for security. If you are using
  JupyterLab this error is because a JupyterLab extension has not yet been written.

 

In [4]:

    

# visualize the first prediction's explanation
shap.plots.force(shap_values)


    

    
Out[4]:







  Visualization omitted, Javascript library not loaded!

  Have you run `initjs()` in this notebook? If this notebook was from another
  user you must also trust this notebook (File -> Trust notebook). If you are viewing
  this notebook on github the Javascript has been stripped for security. If you are using
  JupyterLab this error is because a JupyterLab extension has not yet been written.

 

In [5]:

    

# plot the importance of a single feature across all samples
shap.plots.dependence(shap_values[:,"RM"], color=shap_values)


    

In [6]:

    

# plot the global importance of each feature
shap.plots.bar(shap_values)


    

In [7]:

    

# plot the distribution of importances for each feature over all samples
shap.plots.summary(shap_values)