python - 如何从 GaussianProcessClassifier 中提取估计参数 (theta)

Question

# Scale/ Normalize Independent Variables 
X = StandardScaler().fit_transform(X) 
#Split data into train an test set at 50% each
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=42) 
gpc= GaussianProcessClassifier(1.0 * RBF(1.0), n_jobs=-1)
gpc.fit(X_train,y_train)
y_proba=gpc.predict_proba(X_test)
#classify as 1 if prediction probablity greater than 15.8%
y_pred = [1 if x >= .158 else 0 for x in y_proba[:, 1]]

以上代码按预期运行。但是，为了解释模型，例如“Beta1 中 1 个单位的变化将导致成功概率提高 .7%”，我需要能够看到 theta。我该怎么做呢？感谢您的协助。顺便说一句，这是为了家庭作业

Answer 1

非常好的问题。您确实可以访问 thetas 但是在文档中并不清楚如何执行此操作。

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使用以下内容。我在这里使用 iris 数据集。

from sklearn.gaussian_process.kernels import RBF
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# Scale/ Normalize Independent Variables 
X = StandardScaler().fit_transform(X) 

#Split data into train an test set at 50% each
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= .5, random_state=42) 

gpc= GaussianProcessClassifier(1.0 * RBF(1.0), n_jobs=-1)
gpc.fit(X_train,y_train)
y_proba=gpc.predict_proba(X_test)

#classify as 1 if prediction probablity greater than 15.8%
y_pred = [1 if x >= .158 else 0 for x in y_proba[:, 1]]

# thetas
gpc.kernel_.theta

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结果:

array([7.1292252 , 1.35355145, 5.54106817, 0.61431805, 7.00063873,
       1.3175175 ])

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可以找到文档中访问 thetas 的示例 HERE

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希望这对您有所帮助。