TabularPredictor.predict_proba_multi

TabularPredictor.predict_proba_multi(data=None, models: List[str] | None = None, as_pandas: bool = True, as_multiclass: bool = True, transform_features: bool = True, inverse_transform: bool = True) → dict

Returns a dictionary of prediction probabilities where the key is the model name and the value is the model’s prediction probabilities on the data.

Equivalent output to: ``` predict_proba_dict = {} for m in models:

predict_proba_dict[m] = predictor.predict_proba(data, model=m)

```

Note that this will generally be much faster than calling self.predict_proba separately for each model because this method leverages the model dependency graph to avoid redundant computation.

Parameters:

• data (str or DataFrame, default = None) –

  The data to predict on. If None:

  If self.has_val, the validation data is used. Else, the out-of-fold prediction probabilities are used.

• models (List[str], default = None) – The list of models to get predictions for. If None, all models that can infer are used.

• as_pandas (bool, default = True) – Whether to return the output of each model as a pandas object (True) or numpy array (False). Pandas object is a DataFrame if this is a multiclass problem or as_multiclass=True, otherwise it is a Series. If the output is a DataFrame, the column order will be equivalent to predictor.class_labels.

• as_multiclass (bool, default = True) –

  Whether to return binary classification probabilities as if they were for multiclass classification.

  Output will contain two columns, and if as_pandas=True, the column names will correspond to the binary class labels. The columns will be the same order as predictor.class_labels.

  If False, output will contain only 1 column for the positive class (get positive_class name via predictor.positive_class). Only impacts output for binary classification problems.

• 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).

• inverse_transform (bool, default = True) – If True, will return prediction probabilities in the original format. If False (advanced), will return prediction probabilities in AutoGluon’s internal format.

Return type:

Dictionary with model names as keys and model prediction probabilities as values.