Manual Outlier Threshold
Feature Description
The Manual Threshold Setting feature in Outliers Analysis allows users to take control of outlier detection by defining thresholds tailored to their dataset's nuances and domain knowledge. This customization empowers users to fine-tune the sensitivity of the outlier detection algorithm, resulting in accurate identification of anomalies.
End User Business Benefits
Customized Outlier Detection
Manual threshold setting provides users the control to tailor the outlier detection process to their specific requirements and domain expertise.
Domain-Specific Insights
Define thresholds based on domain knowledge to gain deeper insights into data patterns, uncovering outliers that are contextually relevant and significant.
Improved Data Accuracy
Fine-tuning outlier detection through manual threshold setting improves data accuracy by removing or flagging data points that may skew analyses or lead to incorrect conclusions.
Use Cases
Financial Data Analysis
You can utilize the manual threshold setting to identify unusual transaction amounts or anomalies in financial data, such as fraudulent activities or suspicious transactions.
Sensor Data Monitoring
You can customize the outlier threshold to proactively detect abnormal readings from sensors, enabling timely identification of equipment malfunctions or abnormal conditions.
Customer Behaviour Analysis
You can define outlier thresholds to spot unusual customer behaviours or spending patterns, helping businesses identify outliers for targeted marketing or personalized customer strategies.