我在尝试使用GridCV来确定sklearn中高斯过程回归（GPR）的超参数时，遇到了以下错误：
ValueError: continuous is not supported

欢迎提供任何见解。我的代码如下：

import numpy as npfrom sklearn.gaussian_process import GaussianProcessfrom sklearn.gaussian_process import regression_models as regressionfrom sklearn.gaussian_process import correlation_models as correlationfrom sklearn.datasets import make_regressionfrom sklearn.utils.testing import assert_greater, assert_true, raisesfrom sklearn.model_selection import GridSearchCVb, kappa, e = 5., .5, .1g = lambda x: b - x[:, 1] - kappa * (x[:, 0] - e) ** 2.X = np.array([[-4.61611719, -6.00099547],              [4.10469096, 5.32782448],              [0.00000000, -0.50000000],              [-6.17289014, -4.6984743],              [1.3109306, -6.93271427],              [-5.03823144, 3.10584743],              [-2.87600388, 6.74310541],              [5.21301203, 4.26386883]])y = g(X).ravel()tuned_parameters = [{'corr':['squared_exponential'], 'theta0': [0.01, 0.2, 0.8, 1.]},                    {'corr':['cubic'], 'theta0': [0.01, 0.2,  0.8, 1.]}]scores = ['precision', 'recall']xy_line=(0,1200)for score in scores:    print("# Tuning hyper-parameters for %s" % score)    print()gp = GridSearchCV(GaussianProcess(normalize=False), tuned_parameters, cv=5,                   scoring='%s_weighted' % score)gp.fit(X, y)

回答：

精确度和召回率是用于分类的指标，而不是回归。将GridSearchCV中的scoring='%s_weighted' % score更改为类似scoring='r2'的内容，你的错误就会消失。