Logistic Regression
نویسندگان
چکیده
Logistic regression is a technique to map the input feature to the posterior probability for a binary class. The optimal parameter of regression function is obtained by maximizing log likelihood of training data. In this report, we implement two optimization techniques 1) stochastic gradient decent (SGD); 2) limited-memory BroydenFletcherGoldfarbShanno (L-BFGS) to optimize the log likelihood function. We apply logistic regression on classification problems and reproduce the similar experimental result as [1]. We analyze the convergence of SGD methods and show that SGD has the advantage to converage to a reasonably well estimate when learning rate is carefully tuned.
منابع مشابه
A NEW APPROACH FOR PARAMETER ESTIMATION IN FUZZY LOGISTIC REGRESSION
Logistic regression analysis is used to model categorical dependent variable. It is usually used in social sciences and clinical research. Human thoughts and disease diagnosis in clinical research contain vagueness. This situation leads researchers to combine fuzzy set and statistical theories. Fuzzy logistic regression analysis is one of the outcomes of this combination and it is used in situa...
متن کاملComparison of ordinary logistic regression and robust logistic regression models in modeling of pre-diabetes risk factors
Background: Regarding the increased risk of developing type 2 diabetes in pre-diabetic people, identifying pre-diabetes and determining of its risk factors seems so necessary. In this study, it is aimed to compare ordinary logistic regression and robust logistic regression models in modeling pre-diabetes risk factors. Methods: This is a cross-sectional study and conducted on 6460 people, over ...
متن کاملFactors Influencing Drug Injection History among Prisoners: A Comparison between Classification and Regression Trees and Logistic Regression Analysis
Background: Due to the importance of medical studies, researchers of this field should be familiar with various types of statistical analyses to select the most appropriate method based on the characteristics of their data sets. Classification and regression trees (CARTs) can be as complementary to regression models. We compared the performance of a logistic regression model and a CART in predi...
متن کاملSample size determination for logistic regression
The problem of sample size estimation is important in medical applications, especially in cases of expensive measurements of immune biomarkers. This paper describes the problem of logistic regression analysis with the sample size determination algorithms, namely the methods of univariate statistics, logistics regression, cross-validation and Bayesian inference. The authors, treating the regr...
متن کاملComparison of artificial neural network with logistic regression in prediction of tendency to surgical intervention in nurses
Introduction: Logistic regression is one of the modeling methods for bipartite dependent variables. On the other hand, artificial neural network is a flexible method with the least limitation. The importance of growing unnecessary beauty surgeries and the importance of prediction and classification made us consider the present study, with the aim of comparing logistic regression and artificial ...
متن کاملFUZZY LOGISTIC REGRESSION BASED ON LEAST SQUARE APPROACH AND TRAPEZOIDAL MEMBERSHIP FUNCTION
Logistic regression is a non-linear modification of the linearregression. The purpose of the logistic regression analysis is tomeasure the effects of multiple explanatory variables which can becontinuous and response variable is categorical. In real life there aresituations which we deal with information that is vague innature and there are cases that are not explainedprecisely. In this regard,...
متن کامل