Name
Title | ||
|---|---|---|
Review of Modern Logistic Regression Methods with Application to Small and Medium Sample Size Problems |
Abstract | ||
|---|---|---|
Logistic regression is one of the most widely applied machine learning tools in binary classification problems. Traditionally, inference of logistic models has focused on stepwise regression procedures which determine the predictor variables to be included in the model. Techniques that modify the log-likelihood by adding a continuous penalty function of the parameters have recently been used when inferring logistic models with a large number of predictor variables. This paper compares and contrasts three popular penalized logistic regression methods: ridge regression, the Least Absolute Shrinkage and Selection Operator (LASSO) and the elastic net. The methods are compared in terms of prediction accuracy using simulated data as well as real data sets. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-17432-2_22 | AI 2010: ADVANCES IN ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
Logistic regression,Variable Selection,LASSO,Elastic Net,Ridge regression | Binomial regression,Logistic distribution,Regression diagnostic,Multinomial logistic regression,Elastic net regularization,Logistic model tree,Generalised logistic function,Statistics,Logistic regression,Mathematics | Conference |
Volume | ISSN | Citations |
6464 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 2 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enes Makalic | 1 | 55 | 11.54 |
| D. F. Schmidt | 2 | 11 | 1.28 |