--- license: mit library_name: sklearn tags: - sklearn - skops - tabular-classification widget: structuredData: x0: - 0.6666666666666667 - 1.0 - 1.0 x1: - 0.0 - 0.0 - 0.0 x10: - 0.0 - 0.0 - 0.0 x11: - 0.0 - 1.0 - 0.0 x12: - 1.0 - 0.0 - 1.0 x13: - 0.0 - 0.0 - 0.0 x14: - 0.0 - 0.0 - 0.0 x15: - 1.0 - 0.0 - 0.0 x16: - 0.0 - 0.0 - 0.0 x17: - 0.0 - 0.0 - 1.0 x18: - 0.0 - 0.0 - 0.0 x19: - 0.0 - 1.0 - 0.0 x2: - 1.0 - 1.0 - 1.0 x20: - 1.0 - 0.0 - 0.0 x21: - 0.0 - 1.0 - 1.0 x22: - 0.0 - 0.0 - 0.0 x23: - 1.0 - 0.0 - 1.0 x24: - 0.0 - 0.0 - 0.0 x25: - 0.0 - 0.0 - 0.0 x26: - 0.0 - 0.0 - 0.0 x27: - 0.0 - 1.0 - 0.0 x3: - 0.0 - 1.0 - 0.0 x4: - 0.0 - 0.0 - 1.0 x5: - 1.0 - 0.0 - 0.0 x6: - 0.0 - 0.0 - 0.0 x7: - 0.24999999999999997 - 0.14285714285714285 - 0.3571428571428571 x8: - 0.4772654358070523 - 0.47033921746222385 - 0.32320252247170167 x9: - 0.0 - 0.0 - 0.0 --- # Model description This is a Random Forest model trained on entire set of features from data provided by Reunion. ## Intended uses & limitations This model is not fine-tuned for production. ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters.
Click to expand | Hyperparameter | Value | |-------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | cv | 3 | | error_score | nan | | estimator__bootstrap | True | | estimator__ccp_alpha | 0.0 | | estimator__class_weight | balanced | | estimator__criterion | gini | | estimator__max_depth | | | estimator__max_features | auto | | estimator__max_leaf_nodes | | | estimator__max_samples | | | estimator__min_impurity_decrease | 0.0 | | estimator__min_impurity_split | | | estimator__min_samples_leaf | 1 | | estimator__min_samples_split | 2 | | estimator__min_weight_fraction_leaf | 0.0 | | estimator__n_estimators | 100 | | estimator__n_jobs | -1 | | estimator__oob_score | False | | estimator__random_state | 42 | | estimator__verbose | 1 | | estimator__warm_start | False | | estimator | RandomForestClassifier(class_weight='balanced', n_jobs=-1, random_state=42, verbose=1) | | n_iter | 100 | | n_jobs | -1 | | param_distributions | {'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000], 'max_features': ['auto', 'sqrt'], 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4], 'bootstrap': [True, False]} | | pre_dispatch | 2*n_jobs | | random_state | 42 | | refit | True | | return_train_score | False | | scoring | | | verbose | 2 |
### Model Plot The model plot is below. 
RandomizedSearchCV(cv=3,estimator=RandomForestClassifier(class_weight='balanced',n_jobs=-1, random_state=42,verbose=1),n_iter=100, n_jobs=-1,param_distributions={'bootstrap': [True, False],'max_depth': [10, 20, 30, 40, 50, 60,70, 80, 90, 100, 110,None],'max_features': ['auto', 'sqrt'],'min_samples_leaf': [1, 2, 4],'min_samples_split': [2, 5, 10],'n_estimators': [200, 400, 600, 800,1000, 1200, 1400, 1600,1800, 2000]},random_state=42, verbose=2)
RandomForestClassifier(class_weight='balanced', n_jobs=-1, random_state=42,verbose=1)
## Evaluation Results You can find the details about evaluation process and the evaluation results. | Metric | Value | |----------|---------| | accuracy | 0.705 | | recall | 0.05 | # How to Get Started with the Model Use the code below to get started with the model.
Click to expand ```python import pickle with open(dtc_pkl_filename, 'rb') as file: clf = pickle.load(file) ```
# Model Card Authors This model card is written by following authors: kushkul # 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:** ``` bibtex @inproceedings{...,year={2022}} ``` # Additional Content ## confusion_matrix ![confusion_matrix](confusion_matrix.png)