Related Topics
KNearest Neighbour  Machine Learning
Some more details about the KNearest Neighbors (KNN) algorithm:

Distance Metrics: KNN typically uses distance metrics such as Euclidean distance, Manhattan distance, or Minkowski distance to measure the similarity between data points in the feature space.

Hyperparameter 'k': The choice of the hyperparameter 'k' significantly influences the performance of the KNN algorithm. A small value of 'k' may lead to high model variance (overfitting), while a large value of 'k' may result in high bias (underfitting).

Decision Boundary: In classification tasks, KNN determines the class of a new data point based on the majority class of its 'k' nearest neighbors. This results in decision boundaries that are nonlinear and can adapt to the shape of the data.

Scaling: It's essential to scale the features before applying KNN because the algorithm is sensitive to the scale of the features. Standardization or normalization techniques are commonly used to ensure that all features contribute equally to the distance calculation.

Computational Complexity: One drawback of KNN is its computational complexity, especially with large datasets. Since the algorithm requires storing all training data points, prediction time can be slow, particularly as the dataset size grows.

Imbalanced Data: KNN may struggle with imbalanced datasets, where one class significantly outnumbers the others. In such cases, the majority class may dominate the prediction, leading to biased results. Techniques like oversampling, undersampling, or using weighted distance metrics can help mitigate this issue.
Overall, KNearest Neighbors is a versatile algorithm known for its simplicity and effectiveness, particularly for small to mediumsized datasets with relatively low dimensionality.