K-Nearest Neighbor (KNN) is a classification algorithm used in machine learning to find the k closest data points to a new observation and classify it based on the majority class of those k points.
K-Nearest Neighbor (KNN) is a machine learning algorithm that is used for classification and regression analysis. It is a non-parametric algorithm that does not make any assumptions about the underlying data distribution. KNN is a simple algorithm that is easy to understand and implement.
KNN works by finding the K nearest neighbors to a given data point. The value of K is a hyperparameter that needs to be set before training the model. The distance between the data points is calculated using a distance metric such as Euclidean distance or Manhattan distance.Once the K nearest neighbors are identified, the algorithm assigns the class label of the majority of the neighbors to the data point. In the case of regression analysis, the algorithm calculates the average of the K nearest neighbors and assigns it as the predicted value.
One of the main advantages of KNN is that it is a simple algorithm that is easy to understand and implement. It does not require any training data and can be used for both classification and regression analysis. KNN is also a non-parametric algorithm, which means that it does not make any assumptions about the underlying data distribution.Another advantage of KNN is that it can be used for both linear and non-linear data. It is also a robust algorithm that can handle noisy data and outliers.
One of the main disadvantages of KNN is that it can be computationally expensive, especially when dealing with large datasets. The algorithm needs to calculate the distance between each data point, which can be time-consuming.Another disadvantage of KNN is that it is sensitive to the value of K. If K is set too low, the algorithm may overfit the data, while if K is set too high, the algorithm may underfit the data.
K-Nearest Neighbor (KNN) is a simple and effective machine learning algorithm that can be used for classification and regression analysis. It is a non-parametric algorithm that does not make any assumptions about the underlying data distribution. KNN is a robust algorithm that can handle noisy data and outliers. However, it can be computationally expensive and sensitive to the value of K.