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
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(
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
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=seed)

ebm = ExplainableBoostingClassifier(), y_train)
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()