(all in one line)
and would then get the results for a majority voting of the three results for Praat, AST and Wav2vec2 features.

Other methods are mean, max, sum, max_class, uncertainty_threshold, uncertainty_weighted, confidence_weighted:

majority_voting: The modality function for classification: predict the category that most classifiers agree on.

mean: For classification: compute the arithmetic mean of probabilities from all predictors for each labels, use highest probability to infer the label.

max: For classification: use the maximum value of probabilities from all predictors for each labels, use highest probability to infer the label.

sum: For classification: use the sum of probabilities from all predictors for each labels, use highest probability to infer the label.

max_class: For classification: compare the highest probabilities of all models across classes (instead of same class as in max_ensemble) and return the highest probability and the class

uncertainty_threshold: For classification: predict the class with the lowest uncertainty if lower than a threshold (default to 1.0, meaning no threshold), else calculate the mean of uncertainties for all models per class and predict the lowest.

uncertainty_weighted: For classification: weigh each class with the inverse of its uncertainty (1/uncertainty), normalize the weights per model, then multiply each class model probability with their normalized weights and use the maximum one to infer the label.

confidence_weighted: Weighted ensemble based on confidence (1-uncertainty), normalized for all samples per model. Like before, but use confidence (instead of inverse of uncertainty) as weights.