KNN (K-Nearest Neighbors)

Information

• KNN (K-Nearest Neighbors) is a type of machine learning algorithm used for classification and regression.
• It's a non-parametric algorithm, which means it doesn't make any assumptions about the underlying data distribution.
• The algorithm works by finding the K number of nearest data points to a given data point based on a similarity metric, usually Euclidean distance.
• The value of K is typically chosen by the user and determines how many neighbors will be considered when making a prediction.
• For classification problems, the algorithm assigns the class of the majority of the K nearest neighbors to the given data point.
• For regression problems, the algorithm calculates the average of the K nearest neighbors to predict the value of the given data point.
• KNN can handle multi-class classification problems, as well as numerical and categorical data.
• KNN is a lazy algorithm, meaning that it doesn't perform any training or modeling on the data. Instead, it uses the entire dataset for predictions.
• KNN can be sensitive to the scale of the input features, so feature normalization is often necessary.
• KNN has a high computational cost, as it requires a distance calculation between the given data point and every other data point in the dataset.