当一项功能非常重要/重量时该怎么办？
我是数据科学的新手，目前正在尝试预测客户为提供基于订阅的预订管理软件的公司而流失。它的客户是健身房。 我有一个小的历史数据数据集（False 670，True 230），带有2个数值预测指标：年龄（自订阅以来的天数），上个月的活跃天数（客户（健身房）有预订的日子）和1分类：徽标（布尔值，如果客户在软件中上传徽标）。
预测因子遵循与流失的负相关：
- 徽标：0.65
- num_active_days_last_month：0.40
- 年龄：0.3
特征重要性看起来与徽标的重量最大相似。
当我预测时，模型（逻辑回归）将没有徽标的客户归类为搅拌器，甚至认为他们非常活跃。
例如，以下两个客户几乎具有流失的概率：
客户1：
- 徽标：true
- num_active_days_last_month：1
- 岁：30天
客户2：
- 徽标：false
- num_active_days_last_month：22
- age：250天
我知道这是从模型中学到的 。数据集，但在我的脑海中，将如此强大的重视分配给徽标没有意义。 有什么办法可以避免将徽标完全排除在预测因素之外？也许某种程度上会降低其重要性？
预先感谢您提供的任何帮助/建议。
I am new to Data Science and currently am trying to predict customers churn for a company that offers of subscription-based bookings management software. Its customers are gyms.
I have a small unbalanced dataset of a historical data (False 670, True 230) with 2 numerical predictors: age(days since subscription), number of active days in the last month(days on which a customer(gym) had bookings) and 1 categorical: logo (boolean, if a customers uploaded a logo in a software).
Predictors have following negative correlations with churn :
- logo: 0.65
- num_active_days_last_month: 0.40
- age: 0.3
Feature importances look similar with Logo having the most weight.
When I predict, the model (logistic regression) classifies customers without logo as churners, even thought they are quite active.
For example the following two customers have almost the same probability to churn:
Customer 1:
- logo: True
- num_active_days_last_month: 1
- age:30 days
Customer 2:
- logo: False
- num_active_days_last_month: 22
- age: 250 days
I understand that this is what model learned from the dataset, but it just doesn’t make sense in my mind to have such strong importance assigned to something like Logo.
Is there any way I can avoid completely excluding Logo from the predictors? maybe somehow decrease its importance?
Thank you in advance for any help/ suggestions i can get.
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