Anomalies
Identify anomalous feature bins in deployed model data.
The Anomalies view identifies statistical anomalies in feature bins.
Use it to understand where a dataset may be sparse, imbalanced, unusually concentrated, or otherwise unreliable for the deployed model. The output can help you identify regions where more data may be needed, where monitoring may be useful, or where downstream explanations should be interpreted with care.
When to use
| Use case | Description |
|---|---|
| Inspect data coverage | Find feature bins with low counts, high counts, imbalance, or wide ranges. |
| Review model reliability | Identify regions where the model may be less reliable because the data distribution is unusual. |
| Support monitoring | Use anomaly flags to highlight data quality or drift-prone regions. |
| Enrich other views | Combine anomaly output with attribution views to identify highly attributed features that also have anomaly flags. |
Generate the view
Generate the Anomalies view from a deployed model.
curl -X POST https://api.hi.umnai.com/deployed-models/deployedModelId/views \-H "X-Views-Cache-Control: no-cache, no-store" \-H "Authorization: Bearer <token>" \-H "Content-Type: application/json" \-d '{"data": [{"view_type": "ANOMALY","bin_source": "COMPUTE_BINS_FROM_DATASET","data": {"dataset_id": "b9349358f749402498040f6ae89ba555","origin": "DATASET"},"binning_strategy": "FIXED_WIDTH","feature_names": ["age"]}]}'
Response
{"data": [{"view_type": "ANOMALY","justification_data": {"columns": ["feature_name","bin","bin_id","parent_bin","parent_bin_id","count","imbalanced","imbalanced_area","too_low","too_high","too_wide","child_bins"],"index": [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127],"data": [["age","(17.5703125, 18.140625)","5afc25d0c267a8ddb3a593e5d173793d6c286ab827ecd4092b7299d2128df634",0,0,1259,0,0,0,0,0,0],["age","(18.140625, 18.7109375)","e69e4ccaa10bedd152ab903aa60b4e9142abd1b654530e2f2f9d8ddaf5b6ff76",0,0,0,0,0,1,0,0,0],["age","(18.7109375, 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The request needs view_type set to ANOMALY, a dataset reference in data, and a bin_source.
Output
The response follows the shared Response structure format.
The Anomalies view returns two dataframes.
| Dataframe | Description |
|---|---|
view_data | The anomaly table. Each row represents a feature bin and includes count and anomaly flags. |
justification_data | Supporting bin provenance. When rebinning is enabled, this can include child-bin information for bins created during rebinning. |
Interpret the result
The Anomalies view is easiest to read by finding flagged bins first, then checking the count, bin definition, and parent-child relationship.
Start with flagged bins
Use imbalanced, imbalanced_area, too_low, too_high, and too_wide to identify anomalous bins.
Each flag highlights a different kind of distribution issue. A flagged bin does not necessarily mean the model is wrong, but it does indicate a region that may deserve additional review.
Check the affected feature and bin
Use feature_name, bin, and bin_id to identify the feature region that was flagged.
For categorical features, the bin usually represents a category or category group. For continuous features, the bin usually represents a numeric range.
Review data support
Use count to understand how much data is present in the bin.
Low counts can indicate sparse regions. Very high counts or concentrated counts can indicate imbalance. When a bin is both highly attributed in another view and anomalous here, it is a strong candidate for investigation.
Understand rebinning
Use parent_bin and parent_bin_id to understand whether a row was produced by rebinning.
When rebinning is enabled, anomalous bins can be split into child bins and analysed again. Parent-bin fields help connect the rebinned output back to the original bin.
Use anomalies with explanations
The Anomalies view is most useful when combined with attribution views.
For example, if a feature has high attribution in Feature attribution or Module attribution and is also flagged as too_low, too_high, or imbalanced, review that feature carefully before relying on the explanation.
Binning
The Anomalies view first determines feature bins, then runs the anomaly detection algorithm over those bins.
| Bin source | Description |
|---|---|
COMPUTE_BINS_FROM_DATASET | Compute bins from the referenced dataset. |
ESM | Use bins from the ESM model. |
When bin_source is COMPUTE_BINS_FROM_DATASET, use binning_strategy to control how bins are computed.
| Binning strategy | Description |
|---|---|
FIXED_WIDTH | Create bins with fixed-width intervals. |
QUANTILE_DISCRETIZER | Create bins from quantiles of the dataset distribution. |
Options
The Anomalies view supports options for selecting features, controlling binning, enabling rebinning, and tuning anomaly detection.
| Option | Use |
|---|---|
data | Dataset reference used to compute the anomaly view. |
bin_source | Select whether bins come from the dataset or ESM model. |
feature_names | Restrict anomaly detection to specific features. |
binning_strategy | Choose the binning strategy when computing bins from the dataset. |
num_buckets | Set the number of buckets when computing bins from the dataset. |
rebin_parameters | Enable and configure rebinning of anomalous bins. |
anomaly_parameters | Tune anomaly detection thresholds. |
Feature selection
Use feature_names to limit the view to specific features.
This is useful when you already know which features you want to inspect, or when the dataset has many features and you want a smaller response.
Rebinning
Use rebin_parameters when you want anomalous bins to be split and analysed again.
Rebinning can help identify more precise anomalous regions inside a broader anomalous bin.
| Parameter | Description |
|---|---|
max_peaks | Maximum number of peaks to identify within an anomalous bin. |
peak_threshold | Minimum peak height required for a peak to be considered during rebinning. |
max_iteration_rebin | Maximum number of rebinning iterations. |
Anomaly parameters
Use anomaly_parameters to tune how anomaly flags are detected.
| Parameter | Description |
|---|---|
zeta_k | Threshold parameter used for imbalance detection. |
zeta_a | Threshold parameter used for imbalanced-area detection. |
geo_deviation | Geometric deviation threshold used for wide-bin detection. |
Field reference
The Anomalies view returns dataframes, so individual columns are not documented as standalone API schema properties.
view_data
The view_data dataframe contains the anomaly output.
| Field | Description |
|---|---|
feature_name | Name of the feature being analysed. |
bin | String representation of the feature bin. |
bin_id | Unique ID of the bin. |
parent_bin | Parent bin for rebinned rows, when applicable. |
parent_bin_id | ID of the parent bin for rebinned rows, when applicable. |
count | Number of dataset rows in the bin. |
imbalanced | Indicates whether the bin is imbalanced. |
imbalanced_area | Indicates whether the bin has an imbalanced area. |
too_low | Indicates whether the bin count or density is too low. |
too_high | Indicates whether the bin count or density is too high. |
too_wide | Indicates whether the bin is too wide. |
justification_data
The justification_data dataframe contains supporting bin provenance.
| Field | Description |
|---|---|
feature_name | Name of the feature being analysed. |
bin | String representation of the feature bin. |
bin_id | Unique ID of the bin. |
parent_bin | Parent bin for rebinned rows, when applicable. |
parent_bin_id | ID of the parent bin for rebinned rows, when applicable. |
count | Number of dataset rows in the bin. |
child_bins | Child bins created from a parent anomalous bin, when rebinning is enabled. |