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Model description
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Intended uses & limitations
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Training Procedure
Hyperparameters
The model is trained with below hyperparameters.
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| Hyperparameter | Value |
|---|---|
| C | 1.0 |
| class_weight | |
| dual | False |
| fit_intercept | True |
| intercept_scaling | 1 |
| l1_ratio | |
| max_iter | 100 |
| multi_class | auto |
| n_jobs | |
| penalty | none |
| random_state | 0 |
| solver | lbfgs |
| tol | 0.0001 |
| verbose | 0 |
| warm_start | False |
Model Plot
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LogisticRegression(penalty='none', random_state=0)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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LogisticRegression(penalty='none', random_state=0)
Evaluation Results
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How to Get Started with the Model
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Model Card Authors
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Citation
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BibTeX:
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model_card_authors
jirko
model_description
just the temporal regression with reduced input features
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