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Model description

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Intended uses & limitations

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Training Procedure

Hyperparameters

The model is trained with below hyperparameters.

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Hyperparameter Value
bootstrap True
ccp_alpha 0.0
class_weight
criterion gini
max_depth
max_features sqrt
max_leaf_nodes
max_samples
min_impurity_decrease 0.0
min_samples_leaf 1
min_samples_split 2
min_weight_fraction_leaf 0.0
n_estimators 100
n_jobs
oob_score False
random_state
verbose 0
warm_start False

Model Plot

The model plot is below.

RandomForestClassifier()
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Evaluation Results

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

Metric Value

How to Get Started with the Model

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Model Card Authors

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Model Card Contact

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Citation

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