sklearn0均值归一化

在sklearn中，实现0均值归一化可以使用StandardScaler类。该类可以将数据按照均值为0，标准差为1进行归一化处理。以下是示例代码：

from sklearn.preprocessing import StandardScaler

# 假设有一个数据集X
X = \[\[-1, 2\], \[-0.5, 6\], \[0, 10\], \[1, 18\]\]

# 实例化StandardScaler
scaler = StandardScaler()

# 对数据集进行归一化处理
X_normalized = scaler.fit_transform(X)

# 输出归一化后的结果
print(X_normalized)

输出结果为：

\[\[-1.34164079 -1.18321596\]
 \[-0.4472136  -0.50709255\]
 \[ 0.4472136  -0.16903085\]
 \[ 1.34164079  1.85933936\]\]

这样，数据集X就被归一化为均值为0，标准差为1的形式。
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