Use of machine learning to predict creativity among nurses: a multidisciplinary approach

BMC Nursing

Table 4 Classification results of Input variables using machine learning algorithms

Learner	Recall	Precision	F-measure	ROC
Humble Leadership
Naïve Bayes	0.824	0.811	0.817	0.787
IBk	0.816	0.810	0.813	0.784
K-Star	0.896	0.794	0.842	0.784
RandomTree	0.808	0.802	0.805	0.714
Psychological Safety
Naïve Bayes	0.995	0.995	0.994	0.990
IBk	0.861	0.833	0.847	0.834
K-Star	0.848	0.949	0.896	0.975
RandomTree	0.824	0.924	0.871	0.910
Gender (Female)
Naïve Bayes	0.985	0.977	0.981	0.984
IBk	0.947	0.919	0.932	0.882
K-Star	0.756	0.728	0.742	0.606
RandomTree	0.885	0.879	0.882	0.809
Time Commitment (Full Time)
Naïve Bayes	0.987	0.981	0.984	0.988
IBk	0.968	0.950	0.959	0.884
K-Star	0.892	0.860	0.876	0.760
RandomTree	0.975	0.957	0.966	0.881
Tenure (2- Less than one year & 6–10 years or more)
Naïve Bayes	0.962	0.962	0.962	0.997
IBk	0.558	0.690	0.617	0.766
K-Star	0.662	0.714	0.687	0.800
RandomTree	0.735	0.694	0.714	0.778
Age (1- Less Than 25 Years)
Naïve Bayes	0.923	1.000	0.960	0.993
IBk	0.673	0.778	0.722	0.823
K-Star	0.673	0.660	0.667	0.859
RandomTree	0.788	0.774	0.781	0.851

ISSN: 1472-6955