How can I improve the performance of my anomaly detection algorithms?

How can I improve the performance of my anomaly detection algorithms?

To improve performance, consider tuning hyperparameters, using feature selection, and trying different algorithms.

What hyperparameters should I focus on tuning?

Key hyperparameters include the learning rate, number of estimators, and the threshold for anomaly detection.

Are there specific algorithms that work better for certain types of data?

Yes, for time series data, LSTM networks can be effective, while clustering methods like DBSCAN work well for spatial data.

How do I evaluate the performance of my tuned model?

You can use metrics like precision, recall, F1-score, and ROC-AUC to evaluate your model's performance.