Linked Questions

0
votes
0answers
11 views

Feature Selection in Machine Learning [duplicate]

Is it appropriate to conduct feature selection (e.g., Recursive Feature Selection) on a data set IN ADVANCE of model fitting to scale down features for more expedient machine learning model fitting? ...
1
vote
1answer
764 views

Binary Logistic Regression Methods

I have data sample size of almost 15,000 cases. The dependent variable is a dichotomous variable stating whether the patient has the disease or not, Yes=1, and No=0. I have 12 more independent ...
9
votes
1answer
325 views

I Just Ran Two Million Regressions - Integrated Likelihood

I am currently working on trying to implement a method used in a popular paper titled "I Just Ran Two Million Regressions". The basic idea behind it is that there are certain cases where it is not ...
1
vote
1answer
166 views

Gold standard to select predictors for logistic regression [closed]

I have a data set with 50 predictors of categorical and numerical variables and 1 dichotomous outcome. I'd like to perform logistic regression, model it and k-fold cross validate it. However, I have ...
1
vote
0answers
263 views

Logistic regression model selection using misclassification rate in forward selection

RE: Model selection using misclassification rate in forward selection of logistic regression equation A small misclassification error is good. Keep that factor in the model when doing logistic ...
3
votes
1answer
152 views

Binary Logistic regression results

Is it correct to find that an explanatory variable was found to be statistically significant with the chi-square test but insignificant with the logistic regression analysis model?
1
vote
1answer
131 views

Can VIF and backward elimination be used on a logistic regression model?

I'm conducting a study on mandatory reports in the healthcare sector. I've got a sample of 760 visits (690 individual patients ). I will use a binary logistic regression model to see if my independent ...
0
votes
1answer
59 views

what's the efficient way to do feature selection when I have 500 variables [closed]

my datasets have 500 variables, how to quickly verify which independent variables are significant to my dependent variable or my model? what I usually do is to import some of them, and see which one ...
3
votes
0answers
96 views

Is stagewised feature engineering/ selection an invalid approach? What to do when all the features are not ready at one time?

Suppose we want to build a regression or classification model. However, the features (independent variables used) are not all ready at one time. This is very realistic in business, because the data ...
0
votes
0answers
24 views

does stepwise regression only work when there are a few explanatory variables with a significant correlation with the dependent variable?

I understand that stepwise regression is computationally intensive in general but is it only "suitable" in cases where you can ignore several variables from the model due to statistical insignificance,...