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--- |
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license: mit |
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library_name: sklearn |
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tags: |
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- sklearn |
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- skops |
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- tabular-classification |
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widget: |
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structuredData: |
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x0: |
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- 0.6666666666666667 |
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- 1.0 |
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- 1.0 |
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x7: |
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- 0.24999999999999997 |
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- 0.14285714285714285 |
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- 0.3571428571428571 |
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- 0.4772654358070523 |
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- 0.47033921746222385 |
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- 0.32320252247170167 |
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--- |
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# Model description |
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This is a Random Forest model trained on entire set of features from data provided by Reunion. |
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## Intended uses & limitations |
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This model is not fine-tuned for production. |
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## Training Procedure |
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### Hyperparameters |
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The model is trained with below hyperparameters. |
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<details> |
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<summary> Click to expand </summary> |
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| Hyperparameter | Value | |
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|-------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| cv | 3 | |
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| error_score | nan | |
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| estimator__bootstrap | True | |
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| estimator__ccp_alpha | 0.0 | |
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| estimator__class_weight | balanced | |
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| estimator__criterion | gini | |
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| estimator__max_depth | | |
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| estimator__max_features | auto | |
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| estimator__max_leaf_nodes | | |
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| estimator__max_samples | | |
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| estimator__min_impurity_decrease | 0.0 | |
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| estimator__min_impurity_split | | |
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| estimator__min_samples_leaf | 1 | |
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| estimator__min_samples_split | 2 | |
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| estimator__min_weight_fraction_leaf | 0.0 | |
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| estimator__n_estimators | 100 | |
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| estimator__n_jobs | -1 | |
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| estimator__oob_score | False | |
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| estimator__random_state | 42 | |
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| estimator__verbose | 1 | |
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| estimator__warm_start | False | |
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| estimator | RandomForestClassifier(class_weight='balanced', n_jobs=-1, random_state=42, |
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verbose=1) | |
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| n_iter | 100 | |
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| n_jobs | -1 | |
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| 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]} | |
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| pre_dispatch | 2*n_jobs | |
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| random_state | 42 | |
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| refit | True | |
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| return_train_score | False | |
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| scoring | | |
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| verbose | 2 | |
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</details> |
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### Model Plot |
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The model plot is below. |
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<style>#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 {color: black;background-color: white;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 pre{padding: 0;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 div.sk-toggleable {background-color: white;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 div.sk-estimator:hover {background-color: #d4ebff;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 div.sk-item {z-index: 1;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 div.sk-parallel-item:only-child::after {width: 0;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 div.sk-container {display: inline-block;position: relative;}</style><div id="sk-612ecc16-5410-4287-9cca-3bb6bb70aa61" class"sk-top-container"><div class="sk-container"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="e81b924e-93ea-42c0-84fd-af8e0ec97962" type="checkbox" ><label class="sk-toggleable__label" for="e81b924e-93ea-42c0-84fd-af8e0ec97962">RandomizedSearchCV</label><div class="sk-toggleable__content"><pre>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)</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="4a4e6c45-5264-4a41-8fbe-d7cb73b658bb" type="checkbox" ><label class="sk-toggleable__label" for="4a4e6c45-5264-4a41-8fbe-d7cb73b658bb">RandomForestClassifier</label><div class="sk-toggleable__content"><pre>RandomForestClassifier(class_weight='balanced', n_jobs=-1, random_state=42,verbose=1)</pre></div></div></div></div></div></div></div></div></div></div> |
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##Â Evaluation Results |
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You can find the details about evaluation process and the evaluation results. |
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| Metric | Value | |
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|----------|---------| |
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| accuracy | 0.705 | |
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| recall | 0.05 | |
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# How to Get Started with the Model |
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Use the code below to get started with the model. |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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import pickle |
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with open(dtc_pkl_filename, 'rb') as file: |
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clf = pickle.load(file) |
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``` |
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</details> |
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# Model Card Authors |
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This model card is written by following authors: |
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kushkul |
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# Model Card Contact |
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You can contact the model card authors through following channels: |
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[More Information Needed] |
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# Citation |
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Below you can find information related to citation. |
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**BibTeX:** |
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``` |
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bibtex |
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@inproceedings{...,year={2022}} |
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``` |
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# Additional Content |
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## confusion_matrix |
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 |
