English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This study evaluated random forest’s accuracy in predicting violent or criminal behavior of juveniles compared to that of conventional logistic regression using different sets of risk factors. Drawing on the National Longitudinal Study of Adolescent Health (Add Health), we predicted three outcomes – arrests, convictions, and incarcerations – using three sets of predictors, starting with sociodemographic variables only (Model 1) and incrementally adding behavioral/situational (Model 2) and emotional/environmental risk factors (Model 3). Although both prediction methods yielded similar levels of “overall” predictive accuracy (measured by the area under the receiver operating characteristic curve), our balanced random forest model, with a cost ratio of 10 (false negatives) to 1 (false positives), substantially improved prediction of who will be arrested, convicted, and incarcerated, which is of paramount importance for many researchers and practitioners. In addition to its capability to enhance sensitivity (prediction of “true positives”), random forest is more effective in forecasting juvenile criminal behavior than is conventional logistic regression in that the former is less susceptible to the influences of added predictors than is the latter.

Evaluation of Random Forest in Crime Prediction: Comparing Three-Layered Random Forest and Logistic Regression