typo

stemflow/model/AdaSTEM.py

@@ -824,7 +824,7 @@ def predict_proba(
             return_by_separate_ensembles (bool, optional):
                 Experimental function. return not by aggregation, but by separate ensembles.
             logit_agg:
-                Whether to use logit aggregation for the classification task. If True, the model is averaging the probability prediction estimated by all ensembles in logit scale, and then back-tranform it to probability scale. It's recommened to be combinedly used with the CalibratedClassifierCV class in sklearn as a wrapper of the classifier to estimate the calibrated probability. If False, the output is the essentially the proportion of "1s" acorss the related ensembles; e.g., if 100 stixels covers this spatiotemporal points, and 90% of them predict that it is a "1", then the ouput probability is 0.9; Therefore it would be a probability estimated by the spatiotemporal neiborhood.
+                Whether to use logit aggregation for the classification task. If True, the model is averaging the probability prediction estimated by all ensembles in logit scale, and then back-tranforms it to probability scale. It's recommended to be jointly used with the CalibratedClassifierCV class in sklearn as a wrapper of the classifier to estimate the calibrated probability. If False, the output is essentially the proportion of "1s" across the related ensembles; e.g., if 100 stixels covers this spatiotemporal points, and 90% of them predict that it is a "1", then the output probability is 0.9; Therefore it would be a probability estimated by the spatiotemporal neighborhood. Default is False, but can be set to truth for "real" probability averaging.
         Raises:
             TypeError:
                 X_test is not of type pd.core.frame.DataFrame.