01. Explainer Dash

This example runs an app which an interactive user interface to explore the importance of the features using SHAP values.

Note

https://github.com/oegedijk/explainerdashboard

Out:

"\n# Libraries\nfrom sklearn.ensemble import RandomForestClassifier\nfrom explainerdashboard import ClassifierExplainer\nfrom explainerdashboard import ExplainerDashboard\nfrom explainerdashboard.datasets import titanic_survive\nfrom explainerdashboard.datasets import feature_descriptions\n\n# Get data\nX_train, y_train, X_test, y_test = titanic_survive()\n\n# Create and fit model\nmodel = RandomForestClassifier(n_estimators=50, max_depth=10)     .fit(X_train, y_train)\n\n# Configure explainer\nexplainer = ClassifierExplainer(model, X_test, y_test,\n       cats=['Sex', 'Deck', 'Embarked'],\n       descriptions=feature_descriptions,\n       labels=['Not survived', 'Survived'])\n\n# Run\n#ExplainerDashboard(explainer).run()\n"

"""
# Libraries
from sklearn.ensemble import RandomForestClassifier
from explainerdashboard import ClassifierExplainer
from explainerdashboard import ExplainerDashboard
from explainerdashboard.datasets import titanic_survive
from explainerdashboard.datasets import feature_descriptions

# Get data
X_train, y_train, X_test, y_test = titanic_survive()

# Create and fit model
model = RandomForestClassifier(n_estimators=50, max_depth=10) \
    .fit(X_train, y_train)

# Configure explainer
explainer = ClassifierExplainer(model, X_test, y_test,
       cats=['Sex', 'Deck', 'Embarked'],
       descriptions=feature_descriptions,
       labels=['Not survived', 'Survived'])

# Run
#ExplainerDashboard(explainer).run()
"""