# TermsPredictionResult


Container returned by `GAM.predict(type="terms")`.


Usage

``` python
TermsPredictionResult(
    terms,
    se,
    labels=list(),
)
```


Instead of collapsing every smooth's effect into a single linear predictor, each smooth term's contribution is kept separate. This is useful for decomposing a fitted additive model into its constituent partial effects (e.g., to inspect how much of the prediction at a point comes from `s(x1)` versus `s(x2)`) without needing to build partial-effect plots.


## Attributes


`terms: dict[str, numpy.ndarray]`  
Maps each term label (e.g. `"s(x1)"`, `"te(x1, x2)"`, or `"s(x1):group_a"` for factor-`by` smooths) to that term's contribution to the linear predictor, each of shape `(n,)`. Contributions sum (plus the intercept and any parametric terms) to the full linear predictor.

`se: dict[str, numpy.ndarray] or None`  
Maps each term label to its per-term standard error, each of shape `(n,)`. `None` unless `se=True` was passed to `predict()`.

`labels: list[str]`  
Term labels in formula order, matching the keys of `terms` and `se`.


## Attributes

| Name | Description |
|----|----|
| [values](#values) | Sum of all term contributions (overall linear predictor). |

------------------------------------------------------------------------


### values


Sum of all term contributions (overall linear predictor).


`values: NDArray`