Am I justified in dropping this independent variable?
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I'm currently doing churn prediction in R and during EDA, I discovered that a variable, say gender, has 1720 males who don't churn, and 280 males who do. Also, it has 864 females who don't churn, and 136 females who do. So roughly 14% of both genders churn and 86% don't. From what I can see, this variable does nothing to explain churn. Am I justified in dropping this variable before I put it into a predictive model? (I have conducted stepwise logistic regression and this variable isn't included in the final call)
I'm sorry if this seems like a very basic or obvious question. I've just started with ML and didn't find a question like this elsewhere.
machine-learning data-cleaning logistic-regression
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$begingroup$
I'm currently doing churn prediction in R and during EDA, I discovered that a variable, say gender, has 1720 males who don't churn, and 280 males who do. Also, it has 864 females who don't churn, and 136 females who do. So roughly 14% of both genders churn and 86% don't. From what I can see, this variable does nothing to explain churn. Am I justified in dropping this variable before I put it into a predictive model? (I have conducted stepwise logistic regression and this variable isn't included in the final call)
I'm sorry if this seems like a very basic or obvious question. I've just started with ML and didn't find a question like this elsewhere.
machine-learning data-cleaning logistic-regression
New contributor
$endgroup$
add a comment |
$begingroup$
I'm currently doing churn prediction in R and during EDA, I discovered that a variable, say gender, has 1720 males who don't churn, and 280 males who do. Also, it has 864 females who don't churn, and 136 females who do. So roughly 14% of both genders churn and 86% don't. From what I can see, this variable does nothing to explain churn. Am I justified in dropping this variable before I put it into a predictive model? (I have conducted stepwise logistic regression and this variable isn't included in the final call)
I'm sorry if this seems like a very basic or obvious question. I've just started with ML and didn't find a question like this elsewhere.
machine-learning data-cleaning logistic-regression
New contributor
$endgroup$
I'm currently doing churn prediction in R and during EDA, I discovered that a variable, say gender, has 1720 males who don't churn, and 280 males who do. Also, it has 864 females who don't churn, and 136 females who do. So roughly 14% of both genders churn and 86% don't. From what I can see, this variable does nothing to explain churn. Am I justified in dropping this variable before I put it into a predictive model? (I have conducted stepwise logistic regression and this variable isn't included in the final call)
I'm sorry if this seems like a very basic or obvious question. I've just started with ML and didn't find a question like this elsewhere.
machine-learning data-cleaning logistic-regression
machine-learning data-cleaning logistic-regression
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