可解释的回归

在本笔记本中，我们将拟合可解释的提升机（EBM）、线性回归和回归树模型。拟合完成后，我们将利用它们的透明性来理解它们的全局和局部解释。

这个笔记本可以在我们的examples folder在GitHub上找到。

# install interpret if not already installed
try:
    import interpret
except ModuleNotFoundError:
    !pip install --quiet interpret scikit-learn

import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from interpret import show
from interpret.perf import RegressionPerf

from interpret import set_visualize_provider
from interpret.provider import InlineProvider
set_visualize_provider(InlineProvider())

dataset = fetch_california_housing()
X = dataset.data
y = dataset.target
names = dataset.feature_names

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)

探索数据集

from interpret import show
from interpret.data import Marginal

marginal = Marginal(names).explain_data(X_train, y_train, name='Train Data')
show(marginal)