Today, there are not many good measures for detecting influential observations in case of fitting a logistic regression model. So, the purpose this article is to extrapolate from pre-existing deletion diagnostics defined points multiple linear regression, i.e. DFFITS, DFBETAS and Cook's Distance scenario binary model then view multinomial as special same. The threshold determining whether an ob...

This article proposes a method for multiclass classification problems using ensembles of multinomial logistic regression models. A multinomial logit model is used as a base classifier in ensembles from random partitions of predictors. The multinomial logit model can be applied to each mutually exclusive subset of the feature space without variable selection. By combining multiple models the pro...

In binary classification problems it is common for the two classes to be imbalanced: one case is very rare compared to the other. In this paper we consider the infinitely imbalanced case where one class has a finite sample size and the other class’s sample size grows without bound. For logistic regression, the infinitely imbalanced case often has a useful solution. Under mild conditions, the in...

In this report, several experiments have been conducted on a spam data set with Logistic Regression based on Gradient Descent approach. First, the overfitting effect is shown with basic settings (vanilla version). Then Stochastic Gradient Descent and 2-Norm Regularization techniques are both implemented with demonstration of the benefits of these two methods in preventing overfitting. Besides, ...

We introduce Graph-Sparse Logistic Regression, a new algorithm for classification for the case in which the support should be sparse but connected on a graph. We validate this algorithm against synthetic data and benchmark it against L1-regularized Logistic Regression. We then explore our technique in the bioinformatics context of proteomics data on the interactome graph. We make all our experi...

Statistical modeling of presence-only data has attracted much recent attention in the ecological literature, leading to a proliferation of methods, including the inhomogeneous poisson process (IPP) model [15], maximum entropy (Maxent) modeling of species distributions [12] [9] [10], and logistic regression models. Several recent articles have shown the close relationships between these methods ...