#supervised-learning

#supervised-learning

The research focuses on developing fair supervised learning models using different datasets to evaluate performance towards fairness in predictions.

The article discusses enhancing fairness in supervised learning through distributionally robust optimization techniques.

Choosing between supervised and unsupervised learning is critical for successful machine learning projects, influencing data-driven decisions and AI implementations in businesses.

Outlier detection is crucial in various industries like finance, security, and fraud, with Python libraries such as scikit-learn aiding in the process.