Comments (3)
The regression task is not yet implemented. I'm working on it. Should be available in next 1-2 months.
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would be good to make this clear in the documentation :O
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@ArioBattolla @JonathanLehner I've added support for regression. I need to add more metrics (additional metrics #41) However the package should work on regression problems.
from mljar-supervised.
Related Issues (20)
- 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 5
- 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
- AutoML report apply CSS globally
- cant do plots when supervised imported
- Lightgbm train with custom eval metric error
- Optuna Mode fails. Optuna has migrated to optuna-integration. HOT 3
- Get direction of feature weights for binary classification HOT 2
- As of scikit-learn version 1.5.0, TfidfVectorizer does not have the 'stop_words' attribute
- getting TypeError: only integer scalar arrays can be converted to a scalar index on HOT 1
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