sklearn.datasets使用方法

sklearn.datasets是scikit-learn库中用于加载和处理数据集的模块。可以使用fetch_20newsgroups_vectorized函数加载20个新闻组数据集并将其转换为tf-idf向量。使用该函数的默认设置，可以方便地完成tf-idf转换。首先，需要导入相关的库和模块，如下所示：

from sklearn.datasets import fetch_20newsgroups_vectorized
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split

然后，可以使用fetch_20newsgroups_vectorized函数加载数据集，并将其转换为tf-idf向量。示例代码如下：

bunch = fetch_20newsgroups_vectorized(subset='all')
X, y = shuffle(bunch.data, bunch.target)
print(X.shape)  # (18846, 130107)

数据集可以通过shuffle函数进行洗牌，以打乱数据的顺序。接下来，可以使用train_test_split函数将数据集划分为训练集和测试集。示例代码如下：

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
print(X_train.shape)  # (13192, 130107)
print(X_test.shape)  # (5654, 130107)

这样就完成了使用sklearn.datasets.fetch_20newsgroups_vectorized加载数据集并进行划分的过程。\[1\]
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