对pandas 数据进行数据打乱并选取训练机与测试机集

IT界的小小小学生 2020-11-06 01:27:51
pandas 数据 进行 行数 数据


描述

在机器学习中,拿到一堆训练数据一般会需要将数据切分成训练集和测试集,或者切分成训练集、交叉验证集和测试集,为了避免切分之后的数据集在特征分布上出现偏倚,我们需要先将数据打乱,使数据随机排序,然后在进行切分。
需要用的方法如下:
注:df代表一个pd.DataFrame

df = df.sample(frac=1.0): 按100%的比例抽样即达到打乱数据的效果

df = df.reset_index():打乱数据之后index也是乱的,如果你的index没有特征意义的话,直接重置就可以了,否则就在打乱之前把index加进新的一列,再生成无意义的index

train = df.loc[0:a]: 进行切分操作,切分比例看情况定

cv = df.loc[a+1:b]:

test = df.loc[b+1:-1]:

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https://vip01.blog.csdn.net/article/details/93796160

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