预定义的保留分割：与scikit-learn兼容的保留方法工具

将数据集拆分为训练子集和验证子集，供用户根据指定的索引验证。

> `from mlxtend.evaluate import PredefinedHoldoutSplit`

概述

PredefinedHoldoutSplit 类是 scikit-learn 的 KFold 类的替代类，其中 PredefinedHoldoutSplit 类根据用户指定的验证索引将数据集划分为训练集和验证集，而不进行循环。 PredefinedHoldoutSplit 可以用作 scikit-learn 的 GridSearchCV 等的 cv 参数的参数。

如需进行随机划分，请参见相关的 RandomHoldoutSplit 类。

示例 1 -- 迭代预定义的 Holdout Split

from mlxtend.evaluate import PredefinedHoldoutSplit
from mlxtend.data import iris_data

X, y = iris_data()
h_iter = PredefinedHoldoutSplit(valid_indices=[0, 1, 99])

cnt = 0
for train_ind, valid_ind in h_iter.split(X, y):
    cnt += 1
    print(cnt)


1

print(train_ind[:5])
print(valid_ind[:5])


[2 3 4 5 6]
[ 0  1 99]

示例 2 -- 在网格搜索中使用预定义的保留拆分

from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from mlxtend.evaluate import PredefinedHoldoutSplit
from mlxtend.data import iris_data

X, y = iris_data()


params = {'n_neighbors': [1, 2, 3, 4, 5]}

grid = GridSearchCV(KNeighborsClassifier(),
                    param_grid=params,
                    cv=PredefinedHoldoutSplit(valid_indices=[0, 1, 99]))

grid.fit(X, y)


GridSearchCV(cv=<mlxtend.evaluate.holdout.PredefinedHoldoutSplit object at 0x7fb300565610>,
             estimator=KNeighborsClassifier(),
             param_grid={'n_neighbors': [1, 2, 3, 4, 5]})

API

PredefinedHoldoutSplit(valid_indices)

Train/Validation set splitter for sklearn's GridSearchCV etc.

Uses user-specified train/validation set indices to split a dataset
into train/validation sets using user-defined or random
indices.

Parameters

Examples

For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/evaluate/PredefinedHoldoutSplit/

Methods

get_n_splits(X=None, y=None, groups=None)

Returns the number of splitting iterations in the cross-validator

Parameters

Returns

split(X, y, groups=None)

Generate indices to split data into training and test set.

Parameters

Yields