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Feb 06, 2024
2:56 AM
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The k-nearest neighbors (KNN) algorithm uses a basic but effective machine-learning technique to classify and solve regression tasks. It's part of the category of learning based on instances or lazy learning because it does not explicitly train a model during the learning phase. Instead, it records the entire training data set and then makes predictions based on the similarity between the newly created instances and the previous data points.Data Science Course in Pune
Here's a detailed description of how the KNN algorithm operates:
Basic Concept:
The basic idea of KNN is to determine the k data points within the set of training data that is most similar to the test's instance and then to make predictions based on the major classes (for classification) or the mean amount (for regression) of these k neighbors.
Distance Metric:
The selection of the distance metric is vital in KNN because it is the determining factor in how the algorithm evaluates the degree of similarity between two data points. The most commonly used distance metric is the Euclidean distance, however, other alternatives include the Manhattan distance and Minkowski distance, as well as other distances. Distance metrics are generally determined by the nature of the data as well as the particular issue to be solved.
Training Phase:
Contrary to many other machine-learning methods, KNN doesn't have a conventional training phase. Instead, during "training," the algorithm simply records the entire data set and stores every data point and the respective labels.
Prediction Phase:
If a new instance has been classified or anticipated in any way, the algorithm calculates the distance between this new instance and all the data points of the training set by using the distance metric chosen. The algorithm then chooses the k-nearest neighbors based on these distances.
Classification:
When it comes to classification, KNN predicts the class of the new instance based on the largest class of its k-nearest neighbors. This is usually achieved by the use of a voting system, which means that every neighbor "votes" for its class, and the one with the highest number of votes is the class prediction.
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