--- library_name: sklearn tags: - sklearn - skops - tabular-classification model_format: pickle model_file: hw4_mmcar25_classifier.pkl widget: - structuredData: Age: - .nan - 39.0 - 34.0 Contract Length_Annual: - true - true Contract Length_Monthly: - false - false Contract Length_Quarterly: - false - false CustomerID: - 385551.0 - 261335.0 - .nan Gender_Female: - false Gender_Male: - false - true Last Interaction: - 20.0 - 9.0 - 12.0 Payment Delay: - 19.0 - 7.0 - .nan Subscription Type_Basic: - true Subscription Type_Premium: - false - false - false Subscription Type_Standard: - false - false - true Support Calls: - 0.0 - 0.0 - 8.0 Tenure: - 43.0 - 31.0 - 17.0 Total Spend: - 914.15 - 657.3 - 511.21 Usage Frequency: - 23.0 - 28.0 - .nan --- # Model description [More Information Needed] ## Intended uses & limitations [More Information Needed] ## Training Procedure [More Information Needed] ### Hyperparameters
Click to expand | Hyperparameter | Value | |------------------------------|------------------------------------------------------------------| | memory | | | steps | [('Imputer', SimpleImputer()), ('rf', RandomForestClassifier())] | | verbose | False | | Imputer | SimpleImputer() | | rf | RandomForestClassifier() | | Imputer__add_indicator | False | | Imputer__copy | True | | Imputer__fill_value | | | Imputer__keep_empty_features | False | | Imputer__missing_values | nan | | Imputer__strategy | mean | | rf__bootstrap | True | | rf__ccp_alpha | 0.0 | | rf__class_weight | | | rf__criterion | gini | | rf__max_depth | | | rf__max_features | sqrt | | rf__max_leaf_nodes | | | rf__max_samples | | | rf__min_impurity_decrease | 0.0 | | rf__min_samples_leaf | 1 | | rf__min_samples_split | 2 | | rf__min_weight_fraction_leaf | 0.0 | | rf__monotonic_cst | | | rf__n_estimators | 100 | | rf__n_jobs | | | rf__oob_score | False | | rf__random_state | | | rf__verbose | 0 | | rf__warm_start | False |
### Model Plot
Pipeline(steps=[('Imputer', SimpleImputer()), ('rf', RandomForestClassifier())])
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## Evaluation Results | Metric | Value | |----------|----------| | f1 score | 0.983605 | | accuracy | 0.981512 | # 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 and f1.