Model description

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Intended uses & limitations

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Training Procedure

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Hyperparameters

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Hyperparameter	Value
memory
steps	[('clf', RandomForestClassifier())]
verbose	False
clf	RandomForestClassifier()
clf__bootstrap	True
clf__ccp_alpha	0.0
clf__class_weight
clf__criterion	gini
clf__max_depth
clf__max_features	sqrt
clf__max_leaf_nodes
clf__max_samples
clf__min_impurity_decrease	0.0
clf__min_samples_leaf	1
clf__min_samples_split	2
clf__min_weight_fraction_leaf	0.0
clf__monotonic_cst
clf__n_estimators	100
clf__n_jobs
clf__oob_score	False
clf__random_state
clf__verbose	0
clf__warm_start	False

Model Plot

Pipeline(steps=[('clf', RandomForestClassifier())])

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Evaluation Results

Metric	Value
accuracy	0.85355
f1 score	0.765403
precision	0.788767
recall	0.743383

How to Get Started with the Model

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Model Card Authors

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Citation

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eval_method

The model is evaluated using test split, on accuracy, precision, recall and f1.