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	[('scaler', StandardScaler()), ('svm', RandomForestClassifier())]
verbose	False
scaler	StandardScaler()
svm	RandomForestClassifier()
scaler__copy	True
scaler__with_mean	True
scaler__with_std	True
svm__bootstrap	True
svm__ccp_alpha	0.0
svm__class_weight
svm__criterion	gini
svm__max_depth
svm__max_features	sqrt
svm__max_leaf_nodes
svm__max_samples
svm__min_impurity_decrease	0.0
svm__min_samples_leaf	1
svm__min_samples_split	2
svm__min_weight_fraction_leaf	0.0
svm__monotonic_cst
svm__n_estimators	100
svm__n_jobs
svm__oob_score	False
svm__random_state
svm__verbose	0
svm__warm_start	False

Model Plot

Pipeline(steps=[('scaler', StandardScaler()),('svm', RandomForestClassifier())])

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Evaluation Results

Metric	Value
accuracy	0.860392
f1 score	0.769606
precision	0.819363
recall	0.725547

How to Get Started with the Model

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Model Card Authors

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eval_method

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