pkg_pyknnclassifier.evaluate
Module Contents
Functions
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This function calculates evaluation metrics such as accuracy, precision, recall, and F1 score |
- pkg_pyknnclassifier.evaluate.evaluate(y_true, y_pred, metric='accuracy')[source]
This function calculates evaluation metrics such as accuracy, precision, recall, and F1 score for a k-NN model based on true labels and predicted labels. The default metrics to return is accuracy.
Parameters: - y_true (list or array): True labels. - y_pred (list or array): Predicted labels. - metric (str, optional): Metric to compute. Default is ‘accuracy’.
Possible values: ‘accuracy’, ‘precision’, ‘recall’, ‘f1’.
Returns: - float: Value of the specified metric.
Examples: true_labels = [0, 1, 1, 0, 1, 0, 1, 0] predicted_labels = [0, 1, 1, 0, 1, 1, 0, 1] accuracy_result = evaluate_knn_manual(true_labels, predicted_labels, metric=’accuracy’) print(“Accuracy:”, accuracy_result) precision_result = evaluate_knn_manual(true_labels, predicted_labels, metric=’precision’) print(“Precision:”, precision_result)