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	[('Imputer', SimpleImputer()), ('rf', RandomForestClassifier())]
verbose	False
Imputer	SimpleImputer()
rf	RandomForestClassifier()
Imputer__add_indicator	False
Imputer__copy	True
Imputer__fill_value
Imputer__keep_empty_features	False
Imputer__missing_values	nan
Imputer__strategy	mean
rf__bootstrap	True
rf__ccp_alpha	0.0
rf__class_weight
rf__criterion	gini
rf__max_depth
rf__max_features	sqrt
rf__max_leaf_nodes
rf__max_samples
rf__min_impurity_decrease	0.0
rf__min_samples_leaf	1
rf__min_samples_split	2
rf__min_weight_fraction_leaf	0.0
rf__monotonic_cst
rf__n_estimators	100
rf__n_jobs
rf__oob_score	False
rf__random_state
rf__verbose	0
rf__warm_start	False

Model Plot

Pipeline(steps=[('Imputer', SimpleImputer()), ('rf', RandomForestClassifier())])

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Evaluation Results

Metric	Value
f1 score	0.983605
accuracy	0.981512

How to Get Started with the Model

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eval_method

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