TabularPredictor.predict_from_proba¶

TabularPredictor.predict_from_proba(y_pred_proba: pd.DataFrame | np.ndarray, decision_threshold: float | None = None) → pd.Series | np.array[source]¶

Given prediction probabilities, convert to predictions.

Parameters:

y_pred_proba (pd.DataFrame or np.ndarray) – The prediction probabilities to convert to predictions. Obtainable via the output of predictor.predict_proba.
decision_threshold (float, default = None) – The decision threshold used to convert prediction probabilities to predictions. Only relevant for binary classification, otherwise ignored. If None, defaults to predictor.decision_threshold. Valid values are in the range [0.0, 1.0] You can obtain an optimized decision_threshold by first calling predictor.calibrate_decision_threshold(). Useful to set for metrics such as balanced_accuracy and f1 as 0.5 is often not an optimal threshold. Predictions are calculated via the following logic on the positive class: 1 if pred > decision_threshold else 0

Return type:

Array of predictions, one corresponding to each row in given dataset. Either np.ndarray or pd.Series depending on y_pred_proba dtype.

Examples

>>> from autogluon.tabular import TabularPredictor
>>> predictor = TabularPredictor(label='class').fit('train.csv', label='class')
>>> y_pred_proba = predictor.predict_proba('test.csv')
>>>
>>> # y_pred and y_pred_from_proba are identical
>>> y_pred = predictor.predict('test.csv')
>>> y_pred_from_proba = predictor.predict_from_proba(y_pred_proba=y_pred_proba)