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.

<details> <summary> Click to expand </summary>

                   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 |

</details>

Model Plot

The model plot is below.

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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>

## Evaluation Results

You can find the details about evaluation process and the evaluation results.

How to Get Started with the Model

Use the code below to get started with the model.

<details> <summary> Click to expand </summary>

import pickle 
with open(dtc_pkl_filename, 'rb') as file: 
    clf = pickle.load(file)

</details>

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