机器学习-类别不平衡-上下采样(Upsampling and Downsampling)

本文介绍了处理类别不平衡问题的上下采样技术。常见于如垃圾邮件过滤、欺诈检测和疾病筛查等实际场景的数据集中。scikit-learn库提供通过设置`class_weight='balanced'`来调整模型对少数类预测的权重。文章包含代码实现和分析。

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Section I: Brief Introduction on Upsampling/Downsampling

Class imbalance is a quite common problem when working with real-world data-samples from one class or multiple classes are over-represented in a dataset. Intuitively, we can think of several domains where this may occur, such as spam filtering, fraud detection, or screening for diseases.

Here, be warned that one way to deal with imbalanced class proportions during model fitting is a assign a larger penalty to wrong predictions on the minority class . Via scikit-learn, adjusting such a penalty is as convenient as setting the class_weight parameter to class_weight=“balanced”, which is implemented for most classifiers.

FROM
Sebastian Raschka, Vahid Mirjalili. Python机器学习第二版. 南京：东南大学出版社，2018.

Section II: Code and Analyses

第一部分：代码

from sklearn import datasets
from sklearn.model_selection import train_test_split
import numpy as np
from sklearn.utils import resample
import warnings
warnings.filterwarnings("ignore")

#Section 1: Load Breast data, i.e., Benign