Defore 过采样数据显示 0

Question

我正在处理我的数据集并且对此很陌生。下面是代码：

class_col_name='Creditability' 

feature_names=df.columns[df.columns != class_col_name ]
# 70% training and 30% test
X_train, X_test, y_train, y_test = train_test_split(df.loc[:, feature_names], df[class_col_name], test_size=0.3,random_state=1) 
print("Number transactions X_train dataset: ", X_train.shape) 
print("Number transactions y_train dataset: ", y_train.shape) 
print("Number transactions X_test dataset: ", X_test.shape) 
print("Number transactions y_test dataset: ", y_test.shape) 

print("Before OverSampling, counts of label '1': {}".format(sum(y_train == 1))) 
print("Before OverSampling, counts of label '0': {} \n".format(sum(y_train == 0)))

我正在尝试对我的数据集应用过采样，但是当我在过采样之前对其进行计数时，它在输出中显示为 0，但它确实显示数据集有数据：

下面是输出：

Number transactions X_train dataset:  (700, 20)
Number transactions y_train dataset:  (700,)
Number transactions X_test dataset:  (300, 20)
Number transactions y_test dataset:  (300,)
Before OverSampling, counts of label '1': 0
Before OverSampling, counts of label '0': 0

我正在尝试理解输出并对其进行处理。

Answer 1

您可能想确认可能的 class 标签实际上是 0 和 1。您可以尝试

print(y_train.unique())

检查 class 标签是什么。

如果 y_train 是一个 pandas 系列，标签在 [0, 1]，那么我相信最后两行的结果实际上应该等于 [=17= 的大小].如果标签不是整数 0 或 1 那么这就可以解释为什么总和都是 0.

Defore 过采样数据显示 0

Defore oversampling data showing 0

python

classification

machine-learning

oversampling

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