Nearest Neighbor
K-Nearest Neighbors (KNN) is a supervised machine learning algorithm used for classification and regression. The main idea behind the algorithm is to find the k-nearest data points (neighbors) in the feature space to a given data point, and use the majority class (or average value in case of regression) of these neighbors to predict the class (or value) of the given data point.
The algorithm works by:
Saving all the training data points and their corresponding classes (or values in case of regression) in the memory.
For each new data point to classify (or predict value), the algorithm calculates the distance (using a distance metric such as Euclidean distance) to all the training data points.
Select the k-nearest data points to the new data point, where k is a positive integer and a parameter of the algorithm.
Predict the class (or value) of the new data point as the majority class (or average value) of the k-nearest neighbors.
The pros of KNN are:
It is simple to implement and understand.
It is robust to noise and does not make any assumptions about the data distribution.
It can be used for both classification and regression.
The cons of KNN are:
It requires a large amount of memory to store the training data.
The performance of the algorithm is sensitive to the value of k, and the optimal value of k may be different for different datasets.
The algorithm can be affected by irrelevant or redundant features in the data.
The algorithm works by:
Saving all the training data points and their corresponding classes (or values in case of regression) in the memory.
For each new data point to classify (or predict value), the algorithm calculates the distance (using a distance metric such as Euclidean distance) to all the training data points.
Select the k-nearest data points to the new data point, where k is a positive integer and a parameter of the algorithm.
Predict the class (or value) of the new data point as the majority class (or average value) of the k-nearest neighbors.
The pros of KNN are:
It is simple to implement and understand.
It is robust to noise and does not make any assumptions about the data distribution.
It can be used for both classification and regression.
The cons of KNN are:
It requires a large amount of memory to store the training data.
The performance of the algorithm is sensitive to the value of k, and the optimal value of k may be different for different datasets.
The algorithm can be affected by irrelevant or redundant features in the data.
Updated on: 27/01/2023
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