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Do you replace categorical variables with woe in logistic regression?
This is a question regarding a practice or method followed by some of my colleagues. While making a logistic regression model, I have seen people replace categorical variables (or continuous variables which are binned) with their respective Weight of Evidence (WoE).
What does a positive coefficent in logistic regression mean?
The coefficients in the logistic regression represent the tendency for a given region/demographic to vote Republican, compared to a reference category. A positive coefficent means that region is more likely to vote Republican, and vice-versa for a negative coefficient; a larger absolute value means a stronger tendency than a smaller value.
How is Woe related to the natural logarithm?
This link from this community itself is related to somewhat similar query where someone wrote: WoE displays a linear relationship with the natural logarithm of the odds ratio which is the dependent variable in logistic regression.
How is Woe used to compare different variables?
The (univariate) effect of each category on dependent variable can be simply compared across categories and across variables because WoE is standardized value (for example you can compare WoE of married people to WoE of manual workers) It is a “univariate” measure so it does not take into account correlation between independent variables
What do you need to know about logistic regression?
Logistic regression analysis is a statistical technique to evaluate the relationship between various predictor variables (either categorical or continuous) and an outcome which is binary (dichotomous). In this article, we discuss logistic regression analysis and the limitations of this technique.
How are beta parameters used in logistic regression?
The beta parameters in the model are the linear bias of each naive effect (a.k.a. weight-of-evidence) due to the presence of other predictors and they can be interpreted as the linear change in log odds of the particular predictors due to the presence of other predictors.
How is Woe used in variable transformation and selection?
Weight of Evidence (WoE) is powerful technique to perform variable transformation & selection . It is widely used In credit scoring to measure the separation of good vs bad customers. (variables). Advantages :: – Handles missing values Handles outliers the transformation is based on logrithmic value of distribution.