Contents
How is weight of evidence used?
The weight of evidence tells the predictive power of an independent variable in relation to the dependent variable. Since it evolved from credit scoring world, it is generally described as a measure of the separation of good and bad customers. “Bad Customers” refers to the customers who defaulted on a loan.
What is information value in data science?
Information Value analysis is a data exploration technique that helps determine which columns in a data set have predictive power or influence on the value of a specified dependent variable.
What is a weight of evidence?
The weight of evidence approach means that you use a combination of information from several independent sources to give sufficient evidence to fulfil an information requirement.
What does the weight of evidence mean?
The degree to which evidence convinces triers of fact to either accept or reject a factual assertion. Sometimes, the phrase refers to the strength of a single piece of evidence.
How are weight of evidence and information value related?
These two concepts – weight of evidence (WOE) and information value (IV) evolved from the same logistic regression technique. These two terms have been in existence in credit scoring world for more than 4-5 decades. They have been used as a benchmark to screen variables in the credit risk modeling projects such as probability of default.
What does net weight of evidence ( nwoe ) mean?
The net weight of evidence (NWOE) is the difference between the WOEs for the test group () and the control group (), which is equivalent to. The net information value for variable is then defined as. NWOE and NIV work just like IV and WOE, just from an uplift perspective.
How to create a logistic regression model with weight of evidence?
Finally, let us create a logistic regression model with weight of evidence of the coarse classes as the value for the independent variable age. The following are the results generated through a statistical software.
How is information value calculated in a predictive model?
Information value is one of the most useful technique to select important variables in a predictive model. It helps to rank variables on the basis of their importance. The IV is calculated using the following formula : IV = ∑ (% of non-events – % of events) * WOE