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<p><i>The study aims to develop and validate a flexible leadership prediction (FLP) model using supervised learning techniques. The supervised learning techniques including four machine learning (ML) (Naïve Bayes, decision tree, logistic regression, and multilayer perceptron) and four ensemble learning (EL) (Random Forest (RanF), BootStrap Aggregation (Bagg.), AdaBoost (AdaB), and LogitBoost (Lboost)) techniques were employed to develop the prediction models. Also, tenfold cross-validation method was used to validate the flexible leadership prediction model. The results suggested that the model developed using the EL techniques outperformed ML-based prediction models. Particularly, Lboost and RanF emerged as the best techniques for developing the FLP model.</i></p>

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Predicting Leadership Flexibility Using Supervised Learning Techniques

  • 期刊论文

  • 发表日期：

    2025年2月15日

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  • Kusum Lata,
  • Naval Garg

摘要

The study aims to develop and validate a flexible leadership prediction (FLP) model using supervised learning techniques. The supervised learning techniques including four machine learning (ML) (Naïve Bayes, decision tree, logistic regression, and multilayer perceptron) and four ensemble learning (EL) (Random Forest (RanF), BootStrap Aggregation (Bagg.), AdaBoost (AdaB), and LogitBoost (Lboost)) techniques were employed to develop the prediction models. Also, tenfold cross-validation method was used to validate the flexible leadership prediction model. The results suggested that the model developed using the EL techniques outperformed ML-based prediction models. Particularly, Lboost and RanF emerged as the best techniques for developing the FLP model.