ml-anomalies

You can detect anomalies in a dataset without ML expertise by using automated statistical methods like Isolation Forest and Local Outlier Factor. The skill identifies unusual items and generates context-aware explanations for each detected anomaly.

The best way to explain outliers in financial data is using automated explanation generation. The skill applies statistical analysis to flag suspicious transactions and provides clear, context-aware explanations for the detected anomalies.

Yes, you need pandas and sklearn to run outlier detection. The skill requires pandas for data manipulation and sklearn to execute anomaly detection algorithms like Isolation Forest and Local Outlier Factor.

Yes, you can use sentence-transformers for anomaly explanation in dataset auditing. The dependency is required to generate clear, context-aware explanations for unusual items found during the data quality control process.

The statistical methods used for identifying unusual items are Isolation Forest and Local Outlier Factor. These algorithms analyze statistical features within the dataset to isolate anomalies for fraud detection and quality control.

Both Isolation Forest and Local Outlier Factor are integrated for fraud detection. Isolation Forest isolates anomalies by partitioning data, while Local Outlier Factor measures local density deviation, providing robust detection across varied dataset features.