Comments (1)
Sorry, I've moved start_random_models
to set_advanced
method. To set it please call set_advanced
before calling fit
method. Example:
automl = AutoML()
automl.set_advanced(start_random_models=1)
automl.fit(X, y)
I've added docs for AutoML API https://github.com/mljar/mljar-supervised/blob/master/docs/api.md
from mljar-supervised.
Related Issues (20)
- ImportError: cannot import name 'interp' from 'scipy' HOT 7
- Warning from pandas HOT 2
- 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
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from mljar-supervised.