擅长:python、mysql、java
<p><a href="https://scikit-learn.org/stable/modules/generated/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer" rel="nofollow noreferrer">ColumnTransformer</a>的文档
举例说明。
#TODO:创建一个LabelEncoder对象并将其与X中的每个特征相匹配</p>
<pre><code># import preprocessing from sklearn
from sklearn import preprocessing
# 1. INSTANTIATE
# encode labels with value between 0 and n_classes-1.
le = preprocessing.LabelEncoder()
# 2/3. FIT AND TRANSFORM
# use df.apply() to apply le.fit_transform to all columns
X_2 = X.apply(le.fit_transform)
X_2.head()
</code></pre>
<p>如果您希望看到端到端示例,请<a href="https://www.ritchieng.com/machinelearning-one-hot-encoding/" rel="nofollow noreferrer">check</a>。在</p>