写一段支持向量机回归的shap模型解释代码
时间: 2023-11-04 21:04:53 浏览: 280
支持向量机模型Python代码.txt
import pandas as pd
import numpy as np
import shap
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.svm import SVR
# 加载波士顿房价数据集
boston = load_boston()
X, y = boston.data, boston.target
feature_names = boston.feature_names
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
# SVM回归模型训练
svm = SVR(kernel='linear', C=1.0, epsilon=0.1)
svm.fit(X_train, y_train)
# 使用shap解释SVM回归模型
explainer = shap.KernelExplainer(svm.predict, X_train)
shap_values = explainer.shap_values(X_test)
# 打印特征重要性
shap.summary_plot(shap_values, X_test, feature_names=feature_names)
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