> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/sdgm/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/sdgm/_mcp/server. # Prediction Results > Understand the pandas DataFrame returned by a Kumo Relational prediction `client.relational(graph).predict()` returns Kumo Relational prediction results as a pandas DataFrame. The DataFrame columns and number of rows depend on the prediction task. Every result includes `ENTITY`, the identifier of the target entity from the prediction input. Predictions tied to a specific point in time can also include `ANCHOR_TIMESTAMP`, the time from which the prediction is made. Use these identifier fields to join predictions to downstream data instead of relying on row position. The following example assumes that `graph` describes the connected input tables and `query` contains the prediction request. `run_mode="fast"` explicitly selects the default inference mode. ```python from kumo_relational_client import RelationalClient with RelationalClient(url="http://localhost:8000") as client: result = client.relational(graph).predict(query, run_mode="fast") print(result.columns) print(result.head()) ``` ## Result fields The fields that are present depend on the prediction task and the requested outputs. | Field | When present | Description | | ------------------ | -------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------- | | `ENTITY` | All Kumo Relational results | Identifier of the target entity from the prediction input. | | `ANCHOR_TIMESTAMP` | When an anchor time is available | Time from which the prediction is made; together with `ENTITY`, it identifies a time-specific prediction input. | | `PREDICTION` | Binary classification and regression results | Predicted Boolean or numeric value. | | `_PROB` | Binary-classification probabilities | Probability for the named Boolean class, such as `TRUE_PROB`. | | `CLASS` | Multiclass and link-prediction results | Class value or identifier of an entity ranked as a possible link, with the target or identifier dtype preserved by the SDK. | | `SCORE` | Multiclass and link-prediction results | Score for the class or candidate in the same row. | | `PREDICTED` | Multiclass results | `True` for the winning class and `False` for the other candidate classes. | | `FORECAST_STEP` | Forecast results | One-based sequence number of the forecast period. | | `SCORES` | Other score outputs | Ordered score values returned by the model. | | `Q_` | Quantile outputs | Predicted value at the specified quantile level; available levels depend on the requested outputs. | | `EMBEDDINGS` | When embeddings are requested | Model embedding values. | Do not assume that every task returns every field or one row per input entity. The result shape varies by task: * **Binary classification and regression:** One row per entity. * **Multiclass classification:** One row per entity per class. * **Temporal link prediction:** One row per ranked candidate. * **Forecasting:** One row per entity per forecast period. Inspect the returned DataFrame columns before processing optional outputs. For REST integrations, also check the capabilities of the deployed NVIDIA NIM model. > **Note** > > The Python SDK DataFrame uses uppercase `PREDICTION`. > The NVIDIA NIM REST response uses lowercase `prediction` in each prediction item. > Refer to [NIM REST API](/kumo-relational/reference/nim-rest-api) when working with the REST response directly. ## Illustrative shapes Compare these examples to find the columns and row pattern for your task. They show only the column structure; the values depend on the query and the deployed release. **Scalar regression** ```text ENTITY ANCHOR_TIMESTAMP PREDICTION 0 42 2025-01-01T00:00:00Z 128.40 ``` **Boolean classification** ```text ENTITY PREDICTION FALSE_PROB TRUE_PROB 0 42 True 0.21 0.79 ``` **Multiclass classification** ```text ENTITY ANCHOR_TIMESTAMP CLASS SCORE PREDICTED 0 42 2025-01-01T00:00:00Z enterprise 0.73 True 1 42 2025-01-01T00:00:00Z public 0.15 False 2 42 2025-01-01T00:00:00Z small 0.12 False ``` Filter the winning row for each entity with `result[result["PREDICTED"]]`. Do not coerce `CLASS` to a string because it preserves the target column's dtype. **Forecasting** ```text ENTITY ANCHOR_TIMESTAMP PREDICTION FORECAST_STEP 0 42 2025-01-01T00:00:00Z 128.40 1 1 42 2025-01-01T00:00:00Z 134.75 2 2 42 2025-01-01T00:00:00Z 141.20 3 ``` A forecasting result contains one row per entity per forecast step. `FORECAST_STEP` is one-based. **Temporal link prediction with three ranked candidates** ```text ENTITY ANCHOR_TIMESTAMP CLASS SCORE 0 42 2025-01-01T00:00:00Z sku-7 0.91 1 42 2025-01-01T00:00:00Z sku-3 0.84 2 42 2025-01-01T00:00:00Z sku-9 0.72 ``` A prediction query with `RANK TOP 3` returns three possible linked entities for each input entity, ordered from the highest-scoring candidate to the lowest. Preserve the returned row order when presenting ranked candidates for the same entity. ## Explanations When `explain=True`, `predict()` returns an `Explanation` object instead of a bare DataFrame. Its `prediction` attribute contains the prediction DataFrame, `details` contains the complete machine-readable explanation data, and `summary` contains natural-language text when the backend provides it. See [Prediction Explainability](/kumo-relational/user-guide/explain-predictions/prediction-explainability).