小编Ale*_*lex的帖子

我有一个包含约300个点和32个不同标签的数据集,我想通过使用网格搜索和LabelKFold验证绘制其学习曲线来评估LinearSVR模型.

我的代码看起来像这样:

import numpy as np
from sklearn import preprocessing
from sklearn.svm import LinearSVR
from sklearn.pipeline import Pipeline
from sklearn.cross_validation import LabelKFold
from sklearn.grid_search import GridSearchCV
from sklearn.learning_curve import learning_curve
    ...
#get data (x, y, labels)
    ...
C_space = np.logspace(-3, 3, 10)
epsilon_space = np.logspace(-3, 3, 10)  

svr_estimator = Pipeline([
    ("scale", preprocessing.StandardScaler()),
    ("svr", LinearSVR),
])

search_params = dict(
    svr__C = C_space,
    svr__epsilon = epsilon_space
)

kfold = LabelKFold(labels, 5)

svr_search = GridSearchCV(svr_estimator, param_grid = search_params, cv = ???)

train_space = …