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', MinMaxScaler()), ('knn', KNeighborsClassifier(n_neighbors=15))]
verbose	False
scaler	MinMaxScaler()
knn	KNeighborsClassifier(n_neighbors=15)
scaler__clip	False
scaler__copy	True
scaler__feature_range	(0, 1)
knn__algorithm	auto
knn__leaf_size	30
knn__metric	minkowski
knn__metric_params
knn__n_jobs
knn__n_neighbors	15
knn__p	2
knn__weights	uniform

Model Plot

Pipeline(steps=[('scaler', MinMaxScaler()),('knn', KNeighborsClassifier(n_neighbors=15))])

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Evaluation Results

Metric	Value
accuracy	0.854806
f1 score	0.740402
precision	0.795157
recall	0.692702

How to Get Started with the Model

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eval_method

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