Liski, "Variable Selection in Linear Regression: Several Approaches Based on Normalized Maximum Likelihood," Signal Processing, vol. 91, pp. 1671-1692, 2011. [18] C. Giurcaneanu and S. Razavi, "AR Order Selectio
In this paper, the problem of best subset selection in logistic regression is addressed. In particular, we take into account formulations of the problem re
Van Houwelingen JC, Sauerbrei W: Cross-validation, shrinkage and variable selection in linear regression revisited. Open J Stat. 2013, 3: 79-10.4236/ojs.2013.32011. Article Google Scholar Wan Y, Datta S, Conklin DJ, Kong M: Variable selection models based on multiple imputation with an app...
One of such methods is the support vector machine recursive feature elimination (SVM-RFE), which manages to extract the informative genes in classification problems, while it achieves extremely high performance. In this article, we study a variable selection method for regression problems, called SVR...
when two or more independent variables (features) are strongly correlated. This can be a big deal in linear regression problems as it reduces the efficacy of the linear regression coefficient. By implication, you won’t have a clear insight into how the features affect the target variable. ...
Linear Regression is used to predict the value of a variable based on another variable. What are dependent and independent variables in Linear Regression? The variable which is used to predict another variable is called the independent variable whereas the variable that is being predicted is called...
This is the sharing session for my team, the goal is to quick ramp up the essential knowledges for linear regression case to experience how machine learning works during 1 hour. This sharing will recap basic important concepts, introduce runtime environments, and go through the codes on Notebook...
2.5. Multiple objectives in feature selection Other examples of feature selection include Maldonado et al. [9] using Holdout Support Vector Machine (HOSVM) along with a profit-based measure to remove features which had the least impact on profitability. Alternatively, mixed-integer linear programming...
Poverty is a latent variable built using HI as the unit identification, so that an increase in the indicator’s load stands for increased income; the regression coefficients have therefore been reversed to facilitate interpretation of the effect of poverty. The effect estimates on the x axis are...
Linear regression analyses commonly involve two consecutive stages of statistical inquiry. In the first stage, a single ‘best’ model is defined by a specific selection of relevant predictors; in the second stage, the regression coefficients of the winning model are used for prediction and for inf...