--- library_name: sklearn tags: - sklearn - skops - tabular-classification model_format: pickle model_file: NYC_SQF_ARR_MLP.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', MinMaxScaler()), ('mlp', MLPClassifier())] | | verbose | False | | scaler | MinMaxScaler() | | mlp | MLPClassifier() | | scaler__clip | False | | scaler__copy | True | | scaler__feature_range | (0, 1) | | mlp__activation | relu | | mlp__alpha | 0.0001 | | mlp__batch_size | auto | | mlp__beta_1 | 0.9 | | mlp__beta_2 | 0.999 | | mlp__early_stopping | False | | mlp__epsilon | 1e-08 | | mlp__hidden_layer_sizes | (100,) | | mlp__learning_rate | constant | | mlp__learning_rate_init | 0.001 | | mlp__max_fun | 15000 | | mlp__max_iter | 200 | | mlp__momentum | 0.9 | | mlp__n_iter_no_change | 10 | | mlp__nesterovs_momentum | True | | mlp__power_t | 0.5 | | mlp__random_state | | | mlp__shuffle | True | | mlp__solver | adam | | mlp__tol | 0.0001 | | mlp__validation_fraction | 0.1 | | mlp__verbose | False | | mlp__warm_start | False |

### Model Plot

Pipeline(steps=[('scaler', MinMaxScaler()), ('mlp', MLPClassifier())])

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## Evaluation Results | Metric | Value | |-----------|----------| | accuracy | 0.854977 | | f1 score | 0.755843 | | precision | 0.811757 | | recall | 0.707135 | # 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.