What is logistic lasso?

What is logistic lasso?

LASSO is a penalized regression approach that estimates the regression coefficients by maximizing the log-likelihood function (or the sum of squared residuals) with the constraint that the sum of the absolute values of the regression coefficients, ∑ j = 1 k β j , is less than or equal to a positive constant s.

What is IV and woe?

WoE helps check the linear relationship of a feature with its dependent feature to be used in the model. 2. WoE is a good variable transformation method for both continuous and categorical features. IV is a good measure of the predictive power of a feature and it also helps point out the suspicious feature.

Why should Woe be monotonic?

The WoE transformation through monotonic binning provides a convenient way to address each of aforementioned concerns. It is also worth mentioning that a numeric variable and its strictly monotone functions should converge to the same monotonic WoE transformation.

Why do we do woe transformation?

Weight of Evidence (WOE) helps to transform a continuous independent variable into a set of groups or bins based on similarity of dependent variable distribution i.e. number of events and non-events. The transformed variable will be a continuous variable with WOE values. It is same as any continuous variable.

Why do you replace variable with woe in logistic regression?

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). This is supposedly done to establish a monotonic relation between the regressor and dependent variable.

When to use Class 0 in logistic regression?

For logistic regression, focusing on binary classification here, we have class 0 and class 1. To compare with the target, we want to constrain predictions to some values between 0 and 1.

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