The visualizations consume the Interpret API, and is responsible for both displaying explanations and the underlying rendering infrastructure.
Visualizing with the show method#
Interpret exposes a top-level method
show, of which acts as the surface for rendering explanation visualizations. This can produce either a drop down widget or dashboard depending on what’s provided.
Show a single explanation#
For basic use cases, it is good to show an explanation one at a time. The rendered widget will provide a dropdown to select between visualizations. For example, in the event of a global explanation, it will provide an overview, along with graphs for each feature as shown with the code below:
from interpret import set_visualize_provider from interpret.provider import InlineProvider set_visualize_provider(InlineProvider())
import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from interpret.glassbox import ExplainableBoostingClassifier from interpret import show df = pd.read_csv( "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data", header=None) df.columns = [ "Age", "WorkClass", "fnlwgt", "Education", "EducationNum", "MaritalStatus", "Occupation", "Relationship", "Race", "Gender", "CapitalGain", "CapitalLoss", "HoursPerWeek", "NativeCountry", "Income" ] X = df.iloc[:, :-1] y = (df.iloc[:, -1] == " >50K").astype(int) seed = 42 np.random.seed(seed) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=seed) ebm = ExplainableBoostingClassifier() ebm.fit(X_train, y_train)
ExplainableBoostingClassifier()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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ebm_global = ebm.explain_global() show(ebm_global)