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 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"
df = df.sample(frac=0.05)
train_cols = df.columns[0:-1]
label = df.columns[-1]
X = df[train_cols]
y = df[label]

seed = 1
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=seed)

ebm = ExplainableBoostingClassifier(random_state=seed), y_train)
ExplainableBoostingClassifier(feature_names=['Age', 'WorkClass', 'fnlwgt',
                                             'Education', 'EducationNum',
                                             'MaritalStatus', 'Occupation',
                                             'Relationship', 'Race', 'Gender',
                                             'CapitalGain', 'CapitalLoss',
                                             'HoursPerWeek', 'NativeCountry',
                                             'Relationship x HoursPerWeek',
                                             'Occupation x CapitalGain',
                                             'Age x HoursPerWeek',
                                             'fnlwgt x Occupation',
                                             'fnlwgt x Relationship',
                                             'Age x EducationNum'...
                              feature_types=['continuous', 'categorical',
                                             'continuous', 'categorical',
                                             'continuous', 'categorical',
                                             'categorical', 'categorical',
                                             'categorical', 'categorical',
                                             'continuous', 'continuous',
                                             'continuous', 'categorical',
                                             'interaction', 'interaction',
                                             'interaction', 'interaction',
                                             'interaction', 'interaction',
                                             'interaction', 'interaction',
                                             'interaction', 'interaction'],
ebm_global = ebm.explain_global()