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pushing files from first example to the repo

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  1. README.md +239 -0
  2. config.json +183 -0
  3. confusion_matrix.png +0 -0
  4. rf_model.pkl +3 -0
README.md ADDED
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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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+ x1:
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+ ---
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+
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+ # Model description
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+
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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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+
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+ ## Intended uses & limitations
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+
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+ This model is not fine-tuned for production.
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+
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+ ## Training Procedure
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+
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+ ### Hyperparameters
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+
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+ The model is trained with below hyperparameters.
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+
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+ <details>
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+ <summary> Click to expand </summary>
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+
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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 | |
174
+ | verbose | 2 |
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+
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+ </details>
177
+
178
+ ### Model Plot
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+
180
+ The model plot is below.
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+
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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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+
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+ ## Evaluation Results
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+
186
+ You can find the details about evaluation process and the evaluation results.
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+
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+
189
+
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+ | Metric | Value |
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+ |----------|---------|
192
+ | accuracy | 0.705 |
193
+ | recall | 0.05 |
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+
195
+ # How to Get Started with the Model
196
+
197
+ Use the code below to get started with the model.
198
+
199
+ <details>
200
+ <summary> Click to expand </summary>
201
+
202
+ ```python
203
+ import pickle
204
+ with open(dtc_pkl_filename, 'rb') as file:
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+ clf = pickle.load(file)
206
+ ```
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+
208
+ </details>
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+
210
+
211
+
212
+
213
+ # Model Card Authors
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+
215
+ This model card is written by following authors:
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+
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+ kushkul
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+
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+ # Model Card Contact
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+
221
+ You can contact the model card authors through following channels:
222
+ [More Information Needed]
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+
224
+ # Citation
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+
226
+ Below you can find information related to citation.
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+
228
+ **BibTeX:**
229
+ ```
230
+ bibtex
231
+ @inproceedings{...,year={2022}}
232
+ ```
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+
234
+
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+ # Additional Content
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+
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+ ## confusion_matrix
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+
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+ ![confusion_matrix](confusion_matrix.png)
config.json ADDED
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+ "model": {
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+ "file": "rf_model.pkl"
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+ },
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+ "task": "tabular-classification"
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+ }
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+ }
confusion_matrix.png ADDED
rf_model.pkl ADDED
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