如何将数据集拆分为训练集和验证集，保持比率在 类 之间？
how to split a dataset into training and validation set keeping ratio between classes?
我有一个多 class class 化问题，我的数据集是倾斜的，我有 100 个特定 class 的实例，并说 10 个不同的 class ，所以我想在 classes 之间拆分我的数据集保持率，如果我有 100 个特定 class 的实例，并且我希望 30% 的记录进入训练集中，我希望有 30我的 100 条记录的实例代表 class，我的 10 条记录的 3 个实例代表 class 等等。
您可以使用在线文档中的 sklearn StratifiedKFold：
Stratified K-Folds cross validation iterator
Provides train/test
indices to split data in train test sets.
This cross-validation object
is a variation of KFold that returns stratified folds. The folds are
made by preserving the percentage of samples for each class.
>>> from sklearn import cross_validation
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> skf = cross_validation.StratifiedKFold(y, n_folds=2)
>>> len(skf)
2
>>> print(skf)
sklearn.cross_validation.StratifiedKFold(labels=[0 0 1 1], n_folds=2,
shuffle=False, random_state=None)
>>> for train_index, test_index in skf:
... print("TRAIN:", train_index, "TEST:", test_index)
... X_train, X_test = X[train_index], X[test_index]
... y_train, y_test = y[train_index], y[test_index]
TRAIN: [1 3] TEST: [0 2]
TRAIN: [0 2] TEST: [1 3]
这将保留您的 class 比率，以便拆分保留 class 比率，这将适用于 pandas dfs。
如@Ali_m 所建议，您可以使用 StratifiedShuffledSplit 接受分流比参数：
sss = StratifiedShuffleSplit(y, 3, test_size=0.7, random_state=0)
会产生 70% 的拆分。
简单到：
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y,
stratify=y,
test_size=0.25)
我有一个多 class class 化问题，我的数据集是倾斜的，我有 100 个特定 class 的实例，并说 10 个不同的 class ，所以我想在 classes 之间拆分我的数据集保持率，如果我有 100 个特定 class 的实例，并且我希望 30% 的记录进入训练集中，我希望有 30我的 100 条记录的实例代表 class，我的 10 条记录的 3 个实例代表 class 等等。
您可以使用在线文档中的 sklearn StratifiedKFold：
Stratified K-Folds cross validation iterator
Provides train/test indices to split data in train test sets.
This cross-validation object is a variation of KFold that returns stratified folds. The folds are made by preserving the percentage of samples for each class.
>>> from sklearn import cross_validation
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> skf = cross_validation.StratifiedKFold(y, n_folds=2)
>>> len(skf)
2
>>> print(skf)
sklearn.cross_validation.StratifiedKFold(labels=[0 0 1 1], n_folds=2,
shuffle=False, random_state=None)
>>> for train_index, test_index in skf:
... print("TRAIN:", train_index, "TEST:", test_index)
... X_train, X_test = X[train_index], X[test_index]
... y_train, y_test = y[train_index], y[test_index]
TRAIN: [1 3] TEST: [0 2]
TRAIN: [0 2] TEST: [1 3]
这将保留您的 class 比率，以便拆分保留 class 比率，这将适用于 pandas dfs。
如@Ali_m 所建议，您可以使用 StratifiedShuffledSplit 接受分流比参数：
sss = StratifiedShuffleSplit(y, 3, test_size=0.7, random_state=0)
会产生 70% 的拆分。
简单到：
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y,
stratify=y,
test_size=0.25)