使用 GridSearchCV 调整 SVM 超参数

Question

我正在尝试超调支持向量机分类器以准确预测具有较高重叠程度的类。目标是获得 C 的精确值，例如 7.568787 将类分开

处理这个的部分代码如下：

from sklearn.svm import SVC
from scipy.stats import loguniform
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.calibration import CalibratedClassifierCV

parameters = {"C": loguniform(1e-6, 1e+6)}

grid = GridSearchCV(estimator=CalibratedClassifierCV(SVC(kernel = 'rbf', gamma = 'scale', decision_function_shape='ovr', class_weight=None),method='sigmoid', cv=5), param_grid=parameters, refit = True, verbose = 3)

grid.fit(X_train, Y_train)

但是，当我尝试运行代码时，出现以下错误：

ValueError: Parameter grid for parameter (C) needs to be a list or numpy array, but got (<class 'scipy.stats._distn_infrastructure.rv_frozen'>). Single values need to be wrapped in a list with one element.

Answer 1

loguniform 返回一个

你可以这样使用它：

loguniform(1e-6, 1e+6).rvs(#number of samples you want)

.rvs(size)：从分布中抽取随机样本

编辑 管道示例

pipeline = make_pipeline(
    CountVectorizer(stop_words="english"),
    TfidfTransformer(),
    DecisionTreeClassifier(),
)

params = {
        'decisiontreeclassifier__max_depth': (1,5,10,50,250,500,1000,5000),
}

gridSearch_decisiontree = GridSearchCV(pipeline, params, cv=3, iid=False, n_jobs=-1)
gridSearch_decisiontree.fit(X, Y)