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
l1l2py is a Python package to perform variable selection by means of l1l2 regularization with double optimization.
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
Wrapper functions for GUESS
Analysis of the Underlying Dynamics in the Stock Market: Stock Price of Southwest Airlines and Its Relationship with Other Stocks in the Market
Code and Simulations using Bayesian Approximate Kernel Regression (BAKR)
Class to perform cross validation and draw ROC curves for Test and Training data
Variable selection using the ranger random forest R package
Explored data using data visualisation and exploratory data analysis. Used Logistic Regression to create a basic prediction model. Improved model using recursive feature elimination.
Exploratory data analysis, missing value imputation and linear models on carnicoma data
A Python package for generating candidate models for multi-model inference
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