train_labels, test_labels = train_test_split(df, test_size=0.3, stratify=labels, random_state=12345) # This shows that the dataframe has not been stratified correctly. print("Number of unique labels in train: ", len(set(train_labels["y_label"]))) print("Number of unique labels in te...
监督机器学习的关键方面之一是模型评估和验证。当您评估模型的预测性能时,过程必须保持公正。使用train_test_split()数据科学库scikit-learn,您可以将数据集拆分为子集,从而最大限度地减少评估和验证过程中出现偏差的可能性。
应用train_test_split() 您需要导入 train_test_split()和 NumPy 才能使用它们,因此您可以从以下import语句开始: >>> >>> import numpy as np >>> from sklearn.model_selection import train_test_split 现在您已导入,您可以使用它们将数据拆分为训练集和测试集。您将通过单个函数调用同时拆分输入和输出。 使...
StratifiedShuffleSplit(n_splits=10,test_size=None,train_size=None, random_state=None) n_splits:将数据集分成train/test对的组数,可根据需要进行设置,默认为10 train_size和test_size:是用来设置train/test对中train和test所占的比例。 参数random_state控制是将样本随机打乱 函数作用:(1)首先将数据集打乱n...
from sklearn.model_selection import StratifiedShuffleSplit split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42) for train_index, test_index in split.split(housing, housing["income_cat"]): strat_train_set = housing.loc[train_index] ...
n_splits:指定将数据集划分为多少个折(即K的取值)。 shuffle:布尔值,表示是否在划分之前对数据进行洗牌,以确保数据的随机性。 random_state:整数或者 random_state 实例,用于控制数据的洗牌过程。 stratified:是否进行分层抽样,默认值为False。如果设置为True,则会进行分层抽样,即保证每个子集中的样本类别比例与原始...
from sklearn.model_selection import StratifiedShuffleSplit split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42) for train_index, test_index in split.split(housing, housing["income_cat"]): strat_train_set = housing.loc[train_index] ...
Control the size of the subsets with the parameterstrain_sizeandtest_size Determine therandomnessof your splits with therandom_stateparameter Obtainstratified splitswith thestratifyparameter Usetrain_test_split()as a part ofsupervised machine learningprocedures ...
Describe the workflow you want to enable Currently, train_test_split supports stratified sampling for classification problems using the stratify parameter to ensure that the proportion of classes in the training and test sets is balanced...
您将使用scikit-learn 的0.23.1 版,或sklearn. 它有许多用于数据科学和机器学习的包,但在本教程中,您将重点关注model_selection包,特别是函数train_test_split()。 您可以安装sklearn使用pip install: $ python -m pipinstall-U"scikit-learn==0.23.1" ...