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

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

RandomForestClassifier(class_weight='balanced', n_jobs=-1, random_state=42,verbose=1)

##Â 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

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}}

kushkul
/

rf_model_skops