Documentation Index
Fetch the complete documentation index at: /llms.txt
Use this file to discover all available pages before exploring further.
Scoring
pip install tabpfn-extensions
safe_roc_auc_scoresklearn.metrics.roc_auc_score that handles
cases where the input data may have missing classes or binary classification problems.
safe_roc_auc_score(
y_true,
y_score,
**kwargs,
)
| Parameter | Type | Default | Description |
|---|---|---|---|
y_ | array-like of shape (n_samples,) | Required | True binary labels or binary label indicators. |
y_ | array-like of shape (n_samples,) or (n_samples, n_classes) | Required | Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions. |
**kwargs | dict | — | Additional keyword arguments to pass to sklearn.metrics.roc_auc_score. |
| Type | Description |
|---|---|
| — | float: The ROC AUC score. |
ValueError
If there are missing classes in y_true that cannot be handled.
score_classificationscore_classification(
optimize_metric: Literal["roc", "auroc", "accuracy", "f1", "log_loss"],
y_true,
y_pred,
sample_weight = None,
*,
y_pred_is_labels: bool = False,
)
| Parameter | Type | Default | Description |
|---|---|---|---|
optimize_ | Literal[“roc”, “auroc”, “accuracy”, “f1”, “log_loss”] | Required | The metric to use for scoring the predictions. |
y_ | array-like of shape (n_samples,) | Required | True labels or binary label indicators. |
y_ | array-like of shape (n_samples,) or (n_samples, n_classes) | Required | Predicted labels, probabilities, or confidence values. |
sample_ | array-like of shape (n_samples,), default=None | None | Sample weights. |
y_ | bool | False | — |
| Type | Description |
|---|---|
| — | float: The score for the specified metric. |
ValueError
If an unknown metric is specified.
score_regressionscore_regression(
optimize_metric: Literal["rmse", "mse", "mae", "r2"],
y_true,
y_pred,
sample_weight = None,
)
| Parameter | Type | Default | Description |
|---|---|---|---|
optimize_ | Literal[“rmse”, “mse”, “mae”, “r2”] | Required | The metric to use for scoring the predictions. |
y_ | array-like of shape (n_samples,) | Required | True target values. |
y_ | array-like of shape (n_samples,) | Required | Predicted target values. |
sample_ | array-like of shape (n_samples,), default=None | None | Sample weights. |
| Type | Description |
|---|---|
| — | float: The score for the specified metric. |
ValueError
If an unknown metric is specified.Was this page helpful?