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Analyze another fileDetects anomalies in multivariate data using Isolation Forest. Identifies unusual observations that deviate from the pattern of the majority — useful for fraud detection, quality control, and outlier screening.
Use this when you have numeric data and want to find outliers or anomalous records automatically.
If you're looking for clusters (not outliers), use DBSCAN or K-Means.
Built for: Fraud analyst, quality engineer, data scientist, security analyst
Typical data source: Numeric dataset where some records may be anomalous
Multivariate numeric data
Minimum 50 rows · Best with 500-100000 rows
Detects anomalies in multivariate numeric data using Isolation Forest. Identifies outlier observations that deviate from normal patterns, ranks anomalies by severity score, and explains which features drive anomaly isolation through permutation importance analysis.
Distribution of anomaly scores across all observations
Feature space scatter colored by anomaly status
Which features contribute most to anomaly detection
Feature distributions for normal vs anomalous observations
Most anomalous observations ranked by severity
Descriptive statistics for all features
Isolation Forest model parameters and settings
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Need something simpler? Auto Profiler — Just need outlier counts per column
Need more power? Dbscan — Want to cluster data AND identify noise points
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
Run any analysis on your own data — 60+ validated R modules, interactive reports, AI insights, and PDF export.
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