Tag Archives: Model Selection

Linear Regression in R: Abalone Dataset

This tutorial will perform linear regression on a deceptively simple dataset. The abalone dataset from UCI Machine Learning Arvhives comes with the goal of attempting to predict abalone age (through the number of rings on the shell) given various descriptive attributes of the abalone (Shell sizes, weights of whole abalone and parts of shucked abalone). […]

Logistic Regression (R)

Logistic Regression is a type of classification model. In classification models, we attempt to predict the outcome of categorical dependent variables, using one or more independent variables. The independent variables can be either categorical or numerical. Logistic regression is based on the logistic function, which always takes values between 0 and 1. Replacing the dependent […]

Model Selection Schema

There are various model selection criteria in use for picking variables in linear regression. Some are applicable to other models outside of linear regression. Akaike’s Information Criterion – A useful criterion for indicating the amount of information contained within variables, and deciding whether to omit certain variables. AIC draws its justification from Information Theory. Coefficient […]