data=Fea_aug(data)
时间: 2023-09-26 11:10:44 浏览: 44
这段代码很可能是用于进行数据增强的函数调用。其中,"Fea_aug" 可能是一个自定义函数的名称,用于对输入的数据进行某种形式的增强操作,例如添加噪声、旋转、平移、缩放等。输入的变量 "data" 是待增强的数据,函数返回的结果可能是增强后的数据。
需要注意的是,由于我并不知道 "Fea_aug" 函数的具体实现,以上只是一种猜测,具体情况还需要根据实际代码来分析。
相关问题
sns.heatmap(data=fea_corr,square=True,annot=True)
This code creates a heatmap using the seaborn library in Python. The heatmap displays the correlation between different features in a dataset. The 'data' parameter is used to specify the dataset for which the heatmap needs to be created. The 'square' parameter is used to make the heatmap square in shape. The 'annot' parameter is used to display the correlation values on the heatmap.
for fea in numerical_fea: data_train = data_train[data_train[fea+'_outliers']=='正常值'] data_train = data_train.reset_index(drop=True)
This code snippet loops over the numerical features in a dataset and filters out any rows where the feature's outlier status is labeled as "正常值" (which likely means "normal value" in Chinese). The filtered dataset is then reset so that the row indices are sequential.
It's possible that the dataset has been preprocessed prior to this code being run, with a separate step that identifies and labels outliers for each numerical feature. Without knowing more about the dataset and the preprocessing steps, it's hard to say exactly what this code is doing or what the purpose of filtering out outliers might be.
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