> 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 results as a pandas DataFrame. The `ENTITY` field maps each result row to an input entity. Temporal predictions can also include `ANCHOR_TIMESTAMP`. Join downstream data by using these identifier fields instead of relying on row position. ```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 | Entity identifier from the prediction input. | | `ANCHOR_TIMESTAMP` | When an anchor time is available | Prediction anchor time associated with the 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 ranked-candidate identifier. The SDK preserves the target or identifier dtype. | | `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. | | `EMBEDDINGS` | When embeddings are requested | Model embedding values. | Do not assume that every task returns every field or one row per input entity. Binary classification and regression return one row per entity. Multiclass classification returns one row per entity per class, temporal link prediction returns one row per candidate, and forecasting returns one row per forecast timeframe. Inspect the DataFrame columns or the NIM model capabilities before processing optional outputs. The Python SDK DataFrame uses uppercase `PREDICTION`. The NIM REST response uses lowercase `prediction` in each prediction item. Refer to [NIM REST API](/rfm/nim-rest-api) when working with the wire format directly. ## Illustrative shapes 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; 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 `RANK TOP 3` link-prediction query returns three rows per entity, ordered from the highest-scoring candidate to the lowest. These examples show only the column structure. The values depend on the query and the deployed release. ## Classification probabilities Binary-classification probabilities are expanded into separate columns, such as `TRUE_PROB` and `FALSE_PROB`. Multiclass classification instead returns one row per class with `CLASS`, `SCORE`, and `PREDICTED`. ## Ranked results The SDK expands ranked outputs to one row per candidate. `CLASS` contains the candidate identifier and `SCORE` contains its ranking score. Preserve the returned row order when presenting candidates for the same entity. ## Quantiles and forecasts Quantile outputs use columns prefixed with `Q_`. The exact quantile levels depend on the requested inference output. A forecast returns one row per timeframe and uses `FORECAST_STEP` to identify the one-based sequence. ## Explanations When `explain=True`, `predict()` returns an `Explanation` object instead of a bare DataFrame. Its `prediction` attribute contains the prediction DataFrame, `details` contains structured attribution, and `summary` contains natural-language text when the backend provides it. See [Prediction Explainability](/rfm/prediction-explainability).