In this paper, we proposed a LogSum + L2 penalized logistic regression model, and furthermore used a coordinate decent algorithm to solve it. The results of simulations and real experiments indicate that the proposed method is highly competitive among several state-of-the-art methods. Our ...
However, the traditional logistic regression model has two obvious shortcomings, mainly in the following two aspects: 1. Feature selection problem. All or most of the feature coefficients obtained by fitting the logistic regression model are not zero, i.e. all most of the features are related ...
The outline of the remainder of this paper is as follows. Section 2 discusses the general specification of the multinomial logistic regression model, introduces the multiclass penalty, and discusses the clustering mechanism. Section 3 explains the parameter estimation method. Section 4 performs a simul...
www.nature.com/scientificreports OPEN LogSum + L2 penalized logistic regression model for biomarker selection and cancer classification Xiao‑Ying Liu*, Sheng‑Bing Wu, Wen‑Quan Zeng, Zhan‑Jiang Yuan & Hong‑Bo Xu Biomarker selection and cancer classification play ...
We consider a common logistic regression model in the following form: Pr(y=+1|xx,ww)≡σ(xx,ww)=11+e−∑j=1nxjwj. (1) Learning of this model is typically reduced to the optimization of negative log-likelihood function (with added regularization in order to improve generalization and nu...
Bivariate ordered logistic models (BOLMs) are appealing to jointly model the marginal distribution of two ordered responses and their association, given a set of covariates. When the number of categories of the responses increases, the number of global odds ratios (or their re-parametrizations) to...
A routine for conditional logistic regression is not directly available in penalized, but we exploit the fact that the likelihood of a conditional logistic regression model is the same as that of a Cox model with a specific data structure. In the input, we need to specify the response vector...
The first stage of the algorithms integrates the prior information into representative response variables via principal component analysis (PCA), factor analysis or weighted group Lasso penalized logistic regression. In the second stage, penalized linear regression models with Lasso or elastic net are ...
machine-learningneural-networkalgorithmslinear-regressionlassopcaexpectation-maximizationsplineslogistic-regressionbayesiangradient-descentrkhsridge-regressiongaussian-processescox-modelhamiltonian-monte-carlomixed-modelspenalized-regressionmaximum-likelihood-estimationzero-inflated ...
(X,y,test_size=0.25,random_state=42)# Create a Regressor object for logistic regression to output probabilitiesmodel=Regressor(model='logit_proba',penalization='ridge')# Use cross_val_predict to get probability estimates for each foldprobabilities=cross_val_predict(model,X_train,y_train,method=...