--- library_name: sklearn tags: - sklearn - skops - tabular-classification model_format: pickle model_file: NYC_SQF_ARR_SVM.pkl widget: - structuredData: ASK_FOR_CONSENT_FLG: - 1 - 1 - 0 CONSENT_GIVEN_FLG: - 1 - 0 - 0 FIREARM_FLAG: - 0 - 0 - 0 FRISKED_FLAG: - 1 - 1 - 1 ISSUING_OFFICER_RANK: - 10 - 13 - 9 KNIFE_CUTTER_FLAG: - 0 - 0 - 0 OTHER_CONTRABAND_FLAG: - 1 - 0 - 0 OTHER_WEAPON_FLAG: - 0 - 0 - 0 SEARCHED_FLAG: - 1 - 0 - 1 STOP_ID: - 8283 - 4532 - 14787 STOP_LOCATION_PRECINCT: - 90 - 40 - 106 SUPERVISING_OFFICER_RANK: - 12 - 7 - 12 SUSPECT_BODY_BUILD_TYPE: - 2 - 3 - 3 SUSPECT_HEIGHT: - 5.7 - 5.1 - 5.6 SUSPECT_RACE_DESCRIPTION: - 7 - 3 - 7 SUSPECT_REPORTED_AGE: - 22.0 - 20.0 - 30.0 SUSPECT_SEX: - 1 - 2 - 2 SUSPECT_WEIGHT: - 135.0 - 170.0 - 150.0 WEAPON_FOUND_FLAG: - 0 - 0 - 0 __index_level_0__: - 8282 - 4531 - 14786 --- # Model description [More Information Needed] ## Intended uses & limitations [More Information Needed] ## Training Procedure [More Information Needed] ### Hyperparameters

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| Hyperparameter | Value | |--------------------------|----------------------------------------------------------| | memory | | | steps | [('scaler', StandardScaler()), ('svm', SGDClassifier())] | | verbose | False | | scaler | StandardScaler() | | svm | SGDClassifier() | | scaler__copy | True | | scaler__with_mean | True | | scaler__with_std | True | | svm__alpha | 0.0001 | | svm__average | False | | svm__class_weight | | | svm__early_stopping | False | | svm__epsilon | 0.1 | | svm__eta0 | 0.0 | | svm__fit_intercept | True | | svm__l1_ratio | 0.15 | | svm__learning_rate | optimal | | svm__loss | hinge | | svm__max_iter | 1000 | | svm__n_iter_no_change | 5 | | svm__n_jobs | | | svm__penalty | l2 | | svm__power_t | 0.5 | | svm__random_state | | | svm__shuffle | True | | svm__tol | 0.001 | | svm__validation_fraction | 0.1 | | svm__verbose | 0 | | svm__warm_start | False |

### Model Plot

Pipeline(steps=[('scaler', StandardScaler()), ('svm', SGDClassifier())])

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## Evaluation Results | Metric | Value | |-----------|----------| | accuracy | 0.820274 | | f1 score | 0.695733 | | precision | 0.752005 | | recall | 0.647296 | # 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] ``` # eval_method The model is evaluated using test split, on accuracy, precision, recall and f1.