Example Concept: Anomaly detection models can be improved by enhancing data quality, selecting relevant features, and fine-tuning model parameters. Data preprocessing involves cleaning and normalizing the data to remove noise and outliers. Feature selection focuses on identifying the most informative variables that contribute to distinguishing anomalies. Model tuning involves adjusting hyperparameters and using techniques such as cross-validation to optimize the model's performance.