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)
##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}}
Additional Content
confusion_matrix
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