Model description

[More Information Needed]

Intended uses & limitations

[More Information Needed]

Training Procedure

[More Information Needed]

Hyperparameters

Click to expand

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))])

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Evaluation Results

Metric	Value
accuracy	0.821006
f1 score	0.659874
precision	0.847473
recall	0.540276

How to Get Started with the Model

[More Information Needed]

Model Card Authors

This model card is written by following authors:

[More Information Needed]

Model Card Contact

You can contact the model card authors through following channels: [More Information Needed]

Citation

Below you can find information related to citation.

BibTeX:

[More Information Needed]

eval_method

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