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simple_knn

时间: 2025-01-10 12:45:11 浏览: 48

### Simple KNN Algorithm Implementation and Usage K-nearest neighbors (KNN) is one of the simplest yet effective supervised learning algorithms used for both classification and regression tasks. The principle behind this algorithm involves finding the closest points in the training dataset to a new point based on distance metrics such as Euclidean distance. Below demonstrates how simple KNN can be implemented using Python with Scikit-Learn library: ```python from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import train_test_split from sklearn.datasets import load_iris import numpy as np # Load iris dataset as an example iris = load_iris() X = iris.data y = iris.target # Split into training set and test set X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) # Initialize classifier with number of neighbors k=3 knn_classifier = KNeighborsClassifier(n_neighbors=3) # Fit the model knn_classifier.fit(X_train, y_train) # Predicting labels for unseen data predictions = knn_classifier.predict(X_test) print(f'Predicted classes: {predictions}') ``` For evaluating the performance of the KNN classifier, various methods exist including confusion matrix, precision-recall curves, F1 score, etc., but specifically regarding ROC-AUC evaluation which measures area under receiver operating characteristic curve providing insight about true positive rate against false positive rate at different thresholds[^4]. To choose optimal `k` value, cross-validation technique could prove beneficial where multiple rounds of splitting datasets occur ensuring robustness across all possible splits within given data.

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