dataset = datasets["train"] dataset.train_test_split(test_size=0.1) 这里我们对原始的train数据集进行了划分,可以看到数据按照9:1的比例重新进行了划分。 数据选取与过滤 我们可以通过select方法与filter方法分别对数据集中的数据进行选取与过滤,代码如下: # 选取 datasets["train"].select([0, 1]) # 过滤...
dataset = load_dataset('glue', 'mrpc', split='train') dataset Dataset({ features: ['sentence1', 'sentence2', 'label', 'idx'], num_rows: 3668 }) 'train+test'选择两个字段的数据集: train_test_ds = load_dataset('glue', 'mrpc', split='train+test') Dataset({ features: ['sent...
dataset = boolq_dataset["train"] dataset.train_test_split(test_size=0.1, stratify_by_column="label") ''' DatasetDict({ train: Dataset({ features: ['question', 'passage', 'idx', 'label'], num_rows: 8484 }) test: Dataset({ features: ['question', 'passage', 'idx', 'label'], n...
Splits Dataset into Train and Test DatasetsMarko Nagode
dataset.train_test_split(test_size=0.1) 1. 把数据集切分,10%为测试集。 (6)分桶 把数据集均数若干份,取其中的第几份。 dataset.shard(num_shards=5, index=0) 1. (7)列重命名 c = a.rename_column('text', 'newColumn') 1. (8)列删除 ...
Counter(y)# Counter({0: 332, 1: 335, 2: 333})print("原始特征维度:", X.shape)# 原始特征维度: (1000, 25)# 数据划分X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.3) 三、聚类生成器 make_blobs ...
usetrain_test_split()fromsklearn. You’ve learned that, for an unbiased estimation of the predictive performance of machine learning models, you should use data that hasn’t been used for model fitting. That’s why you need to split your dataset into training, test, and in some cases, ...
datasets import load_boston boston = load_boston() X = boston.data # 特征值 y = boston.target # 目标值 # 划分数据集,80% 训练数据和 20% 测试数据 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) X_train.shape, X_test.shape, y_train.shape, y_test...
1.Splitting Datasets With scikit-learn and train_test_split() (Overview)01:04 2.The Importance of Data Splitting03:35 3.How to Install scikit-learn01:47 4.An Introduction to train_test_split()00:25 5.How to Apply train_test_split()04:23 ...
train_dataset=load_dataset("ag_news",split="train[:40000]")dev_dataset=load_dataset("ag_news",split="train[40000:50000]")test_dataset=load_dataset("ag_news",split="test")print(train_dataset)print(dev_dataset)print(test_dataset)