Interpretable Classification#
In this notebook we will fit classification explainable boosting machine (EBM), LogisticRegression, and ClassificationTree models. After fitting them, we will use their glassbox nature to understand their global and local explanations.
This notebook can be found in our examples folder on GitHub.
# install interpret if not already installed
try:
import interpret
except ModuleNotFoundError:
!pip install --quiet interpret pandas scikit-learn
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from interpret import show
from interpret.perf import ROC
from interpret import set_visualize_provider
from interpret.provider import InlineProvider
set_visualize_provider(InlineProvider())
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)
Explore the dataset
from interpret.data import ClassHistogram
hist = ClassHistogram().explain_data(X_train, y_train, name='Train Data')
show(hist)