Interpret is supported across Windows, Mac and Linux on Python 3.5+

pip install interpret

conda install -c conda-forge interpret

git clone && cd interpret/scripts && make install

InterpretML supports training interpretable models (glassbox), as well as explaining existing ML pipelines (blackbox). Let’s walk through an example of each using the UCI adult income classification dataset.

Download and Prepare Data#

First, we will load the data into a standard pandas dataframe or a numpy array, and create a train / test split. There’s no special preprocessing necessary to use your data with InterpretML.

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split

df = pd.read_csv(
df.columns = [
    "Age", "WorkClass", "fnlwgt", "Education", "EducationNum",
    "MaritalStatus", "Occupation", "Relationship", "Race", "Gender",
    "CapitalGain", "CapitalLoss", "HoursPerWeek", "NativeCountry", "Income"
train_cols = df.columns[0:-1]
label = df.columns[-1]
X = df[train_cols]
y = df[label]

seed = 42
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=seed)

Train a Glassbox Model#

Glassbox models are designed to be completely interpretable, and often provide similar accuracy to state-of-the-art methods.

InterpretML lets you train many of the latest glassbox models with the familiar scikit-learn interface.

from interpret.glassbox import ExplainableBoostingClassifier
ebm = ExplainableBoostingClassifier(), y_train)
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Explain the Glassbox#

Glassbox models can provide explanations on a both global (overall behavior) and local (individual predictions) level.

Global explanations are useful for understanding what a model finds important, as well as identifying potential flaws in its decision making (i.e. racial bias).

from interpret import set_visualize_provider
from interpret.provider import InlineProvider
from interpret import show

ebm_global = ebm.explain_global()