OneHotEncoder is a class in the sklearn.preprocessing module of the scikit-learn library ¹. It is used to encode categorical features as a one-hot numeric array ¹. The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features ¹. The features are encoded using a one-hot encoding scheme, which creates a binary column for each category and returns a sparse matrix or dense array (depending on the sparse_output parameter) ¹. By default, the encoder derives the categories based on the unique values in each feature. Alternatively, you can also specify the categories manually ¹. This encoding is needed for feeding categorical data to many scikit-learn estimators, notably linear models and SVMs with the standard kernels ¹. Note that a one-hot encoding of y labels should use a LabelBinarizer instead ¹.OneHotEncoder "是 "scikit-learn "库¹的 "sklearn.preprocessing "模块中的一个类。它用于将分类特征编码为一热数字数组 ¹。该转换器的输入应该是一个整数或字符串数组，表示分类（离散）特征的取值¹。特征使用单次编码方案进行编码，为每个类别创建一个二进制列，并返回稀疏矩阵或密集数组（取决于 `sparse_output` 参数）¹。默认情况下，编码器根据每个特征中的唯一值推导出类别。或者，也可以手动指定类别 ¹。向许多 scikit-learn 估计器（尤其是线性模型和带有标准核的 SVM）输入分类数据时，都需要使用这种编码 ¹。请注意，y 标签的单次编码应使用 "LabelBinarizer "来代替 ¹。