When to remove insignificant variables in logistic regression?
Typically, when you use logistic regression in a business setting, both inferential information about the variables used along with a good prediction are what stakeholders are looking for. Additionally, another good reason to remove variables is for model parsimony.
How to look into feature importance in logistic regression?
After completing a project that looked into winning in PUBG ( https://medium.com/@jasonrichards911/winning-in-pubg-clean-data-does-not-mean-ready-data-47620a50564 ), it occurred to me that different models produced different feature importance rankings.
Is it good to leave insignificant effects in a model?
For that reason, most people recommend leaving those lower-order effects in. The main point here is there are often good reasons to leave insignificant effects in a model. The p-values are just one piece of information. You may be losing important information by automatically removing everything that isn’t significant.
Which is the best model selection in logistic regression?
Not surprising with the levels of model selection (Logistic Regression, Random Forest, XGBoost), but in my Data Science-y mind, I had to dig deeper, particularly in Logistic Regression. Let’s reverse gears for those already about to h i t the back button.
What makes a good logistic regression model successful?
The key to a successful logistic regression model is to choose the correct variables to enter into the model. While it is tempting to include as many input variables as possible, this can dilute true associations and lead to large standard errors with wide and imprecise confidence intervals, or, conversely, identify spurious associations.
How to deal insignificant levels of a categorical variable?
We cannot include some categories of a variable and exclude some categories having insignificant difference. Suppose you have a nominal categorical variable having 4 categories (or levels). You would create 3 dummy variables (k-1 = 4-1 dummy variables) and set one category as a reference level.
Which is the best way to perform Lasso regression?
A better way would be to use the functions in the penalised or glmnet package to perform a lasso regression. What is the correlations among the independent variables? This is less important for pure prediction, but if you want to gain some inferential information it is important that the independent variables be fairly uncorrelated.