Comments (2)
Hi @avr323,
We aggregate predictions from all folds and compute the metrics based on all predictions. We don't keep metrics from each fold.
from mljar-supervised.
Hi,
Thank you for the response. Is there a way to add evaluation metric standard deviation into the metrics output then? Hard to say one model is better than another soley based on individual AUC number if the confidence intervals overlap. People who are using autoML for classification problems should report confidence intervals for this purpose.
from mljar-supervised.
Related Issues (20)
- sklearn/metrics/_scorer.py:548: FutureWarning HOT 2
- Get confidence scores for regression predictions HOT 1
- FutureWarning: The `needs_threshold` and `needs_proba` parameter. HOT 1
- What's the parameter sample_weight used for? HOT 1
- trained error HOT 1
- problem run in colab HOT 2
- UserWarning: The y_pred values do not sum to one. Starting from 1.5 thiswill result in an error.
- report() not working in JupyterLab HOT 1
- Functionality to retrain or continue training models using the library.
- problem with automl._best_model() HOT 1
- Please document all preprocessing methods HOT 4
- Links to models are not working in report
- Google colab - Feature selection not working HOT 7
- Fix issues from AutoML benchmark
- X has feature names, but StandardScaler was fitted without feature names
- 'module' object is not callable HOT 3
- Feature names unseen at fit time HOT 4
- Error after resume training from previous non finished training
- Creating a Simple enough UI that a person from Non-tech background could understand. HOT 3
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from mljar-supervised.