Comments (1)
Added in 0.1.2 release.
Additional metrics are provided with an optimal threshold value.
auc f1 accuracy precision recall mcc
score 0.880636 0.664122 0.838710 0.650485 1.000000 0.559327
threshold NaN 0.256419 0.386162 0.386162 0.004686 0.256419
There is also confusion matrix computed:
Predicted as negative Predicted as positive
Labeled as negative 321 64
Labeled as positive 24 87
To access metrics:
automl._max_metrics
To access confusion matrix:
automl._confusion_matrix
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
- view individual CV metrics or CV metric AUC mean and standard deviations HOT 2
- 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.