TabularPredictor.predict

TabularPredictor.predict(data: str | TabularDataset | DataFrame, model: str | None = None, as_pandas: bool = True, transform_features: bool = True, *, decision_threshold: float | None = None)[source]

Use trained models to produce predictions of label column values for new data.

Parameters:

  • data (str or TabularDataset or pd.DataFrame) – The data to make predictions for. Should contain same column names as training Dataset and follow same format (may contain extra columns that won’t be used by Predictor, including the label-column itself). If str is passed, data will be loaded using the str value as the file path.

  • model (str (optional)) – The name of the model to get predictions from. Defaults to None, which uses the highest scoring model on the validation set. Valid models are listed in this predictor by calling predictor.model_names()

  • as_pandas (bool, default = True) – Whether to return the output as a pd.Series (True) or np.ndarray (False).

  • transform_features (bool, default = True) –

    If True, preprocesses data before predicting with models. If False, skips global feature preprocessing.

    This is useful to save on inference time if you have already called data = predictor.transform_features(data).

  • 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 as_pandas argument.