def METHOD_NAME(self, data_dict):
    if not self.classifier_initialized:
        self.initialize_classifier()
    samples = list(data_dict.keys())
    self.run.info('Num samples to classify', "%d." % (len(samples)))
    data = []
    for sample in samples:
        datum = []
        for feature in self.features:
            if feature not in data_dict[sample]:
                raise ConfigError("RF prediction run into an issue. All features described in the classifier should be present "
                                   "for all observations in the data. However, that is not the case. For instance, feature "
                                   "'%s' is in the classifier, but the entry '%s' in the input data does not have an observation "
                                   "for it :/ Not good." % (feature, sample))
            datum.append(data_dict[sample][feature])
        data.append(datum)
    predictions = self.classifier.predict_proba(data)
    predictions_dict = {}
    for i in range(0, len(samples)):
        sample = samples[i]
        predictions_dict[sample] = {}
        for j in range(0, len(self.classes)):
            _class = self.classes[j]
            predictions_dict[sample][_class] = predictions[i][j]
    return predictions_dict