Home > Research > Publications & Outputs > Can't see the trees for the forest

Research

Electronic data

Keywords

View graph of relations

Can't see the trees for the forest: The benefits of using Random Forests analysis method

Research output: Contribution to conference - Without ISBN/ISSN › Speech

Published

Standard

Can't see the trees for the forest: The benefits of using Random Forests analysis method. / Mills, Emma.
2019. Lancaster University Psychology PhD Conference, Lancaster, United Kingdom.

Research output: Contribution to conference - Without ISBN/ISSN › Speech

Harvard

Mills, E 2019, 'Can't see the trees for the forest: The benefits of using Random Forests analysis method', Lancaster University Psychology PhD Conference, Lancaster, United Kingdom, 28/06/19 - 28/06/19.

APA

Mills, E. (2019). Can't see the trees for the forest: The benefits of using Random Forests analysis method. Lancaster University Psychology PhD Conference, Lancaster, United Kingdom.

Vancouver

Mills E. Can't see the trees for the forest: The benefits of using Random Forests analysis method. 2019. Lancaster University Psychology PhD Conference, Lancaster, United Kingdom.

Author

Mills, Emma. / Can't see the trees for the forest : The benefits of using Random Forests analysis method. Lancaster University Psychology PhD Conference, Lancaster, United Kingdom.

Bibtex

@conference{a59e4119eaea4afda37eed4faabd9652,
title = "Can't see the trees for the forest: The benefits of using Random Forests analysis method",
abstract = "As Psychologists, we are often interested in interaction effects that are small in size. Finding these, however, is a challenge, with even the most advanced statistical methods falling foul of multi collinearity, order effects, singularity and model non-convergence. I share my experience of the Random Forest approach - it allows me to enter all my predictors, co-linear and all, deals with non-normal distributions intuitively and can also assist in variable selection if I ask it to.",
keywords = "Random Forests, Statistical Analysis, Methods",
author = "Emma Mills",
year = "2019",
month = jun,
day = "28",
language = "English",
note = "Lancaster University Psychology PhD Conference : Internal annual conference ; Conference date: 28-06-2019 Through 28-06-2019",

}

RIS

TY - CONF

T1 - Can't see the trees for the forest

T2 - Lancaster University Psychology PhD Conference

AU - Mills, Emma

PY - 2019/6/28

Y1 - 2019/6/28

N2 - As Psychologists, we are often interested in interaction effects that are small in size. Finding these, however, is a challenge, with even the most advanced statistical methods falling foul of multi collinearity, order effects, singularity and model non-convergence. I share my experience of the Random Forest approach - it allows me to enter all my predictors, co-linear and all, deals with non-normal distributions intuitively and can also assist in variable selection if I ask it to.

AB - As Psychologists, we are often interested in interaction effects that are small in size. Finding these, however, is a challenge, with even the most advanced statistical methods falling foul of multi collinearity, order effects, singularity and model non-convergence. I share my experience of the Random Forest approach - it allows me to enter all my predictors, co-linear and all, deals with non-normal distributions intuitively and can also assist in variable selection if I ask it to.

KW - Random Forests

KW - Statistical Analysis

KW - Methods

M3 - Speech

Y2 - 28 June 2019 through 28 June 2019

ER -