--- license: mit library_name: sklearn tags: - sklearn - skops - tabular-classification model_format: pickle model_file: logreg.pkl widget: - structuredData: Age: - 24.0 - 31.0 - 20.0 Pclass: - 3 - 2 - 3 --- # Model description [More Information Needed] ## Intended uses & limitations [More Information Needed] ## Training Procedure [More Information Needed] ### Hyperparameters
Click to expand | 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 | l2 | | random_state | | | solver | lbfgs | | tol | 0.0001 | | verbose | 0 | | warm_start | False |
### Model Plot 
LogisticRegression()
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## Evaluation Results | Metric | Value | |----------|----------| | accuracy | 0.728814 | | f1 score | 0.728814 | # How to Get Started with the Model [More Information Needed] # Model Card Authors This model card is written by following authors: [More Information Needed] # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` [More Information Needed] ``` # citation_bibtex bibtex @inproceedings{...,year={2024}} # get_started_code import pickle with open(dtc_pkl_filename, 'rb') as file: clf = pickle.load(file) # model_card_authors skops_user # limitations This model is ready to be used to production in titanic dataset. # model_description This is a LogisticRegression model trained on titanic dataset. # eval_method The model is evaluated using test split, on accuracy and F1 score with macro average. # confusion_matrix ![confusion_matrix](confusion_matrix.png)