How would a skewed variable impact a classification problem (logistic regression, tree model)?
Is it justified to bin the skewed variable ?
My data set comprises of younger demographic and fewer older candidates which is expected since it is about college goers.
Also, since college goers are above the age of 19 could this data be considered censored ? And what impact would this have on classification ?
Note: It is important for me to understand the impact of age if any on the classification.
While this When should we discretize/bin continuous independent variables/features and when should not? advises against binning and the references talk about the Modifiable Areal Unit problem, this Why Binning Variables in Predictive Analytics? suggests that (1) There is implicit binning in decision trees and (2) Quotes an example of the impact an optimal range of temperature has on flowering which cannot be captured by continuous variable.
In my problem I am trying to learn the impact of age among other variables on graduating and most college goers are young except a few older students.
If I am building a tree model for classification, should the age be considered a skewed variable and does binning help in building a tree or is detrimental to the process of splits using entropy or gini-index ?
Wikipedia article on Binning says:
Binning is also used in machine learning to speed up the decision-tree boosting method for supervised classification and regression in algorithms such as Microsoft's LightGBM and scikit-learn's Histogram-based Gradient Boosting Classification Tree
Both posts seems to agree that there is loss of information when continuous variables are binned.