An R package for selecting variables in regression models
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Updated
Dec 21, 2015 - R
An R package for selecting variables in regression models
R package for Variable Selection, Curve Fitting, Variable Conversion and Normalisation
Various variable selection methods are explored
R package for generalized knockoffs filter for controlled variable selection
Applying Linear regression for car price prediction and key variable identification
Applying logistic regression to predict employee attrition and understand key contributors to attrition
All independent variables do not have the similar impact on dependent variable. Here we will try to find the independent varibles that have most significant impact on dependent variable to make the ML algorithm fast and accurate by utilizing RFE.
All independent variables do not have the similar impact on dependent variable. Here we will try to find the independent varibles that have most significant impact on dependent variable to make the ML algorithm fast and accurate by utilizing LASSO.
Replication of an mQTL analysis using the ``locus'' method on simulated data
Case studies for testing the risk of overfitting and the need for variable selection in spatial (-temporal) predictive modelling
Analysis of the Underlying Dynamics in the Stock Market: Stock Price of Southwest Airlines and Its Relationship with Other Stocks in the Market
Class to perform cross validation and draw ROC curves for Test and Training data
Variable selection using the ranger random forest R package
Exploratory data analysis, missing value imputation and linear models on carnicoma data
R script to rank and select variables based on their importance/predictive power
Predicting a startup's profitability with a linear regression model in R
implement BCD to find l1-penalized MLE with appropriate parametrization multivariate GLM
To study what factors and how they would impact the landing distance of a commercial flight
A Recalibrated Hypothesis Test for SNP-Level Summary Statistics
The MCB for variable selection identifies two nested models (upper and lower confidence bound models) containing the true model at a given confidence level.
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