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Slide 13.21: An example of decision tree using Sklearn Slide 14.2: K‐nearest neighbor (kNN) algorithm (cont.) Home         Print version

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K-Nearest Neighbor (kNN) Algorithm K-nearest neighbor is a supervised learning algorithm where the result of new instance query is classified based on majority of K-nearest neighbor category. The classifiers do not use any model to fit and only based on memory. Within AI, there are two basic approaches: supervised learning and unsupervised learning. The main difference is one uses labeled data to help predict outcomes, while the other does not. Supervised Learning It is defined by its use of labeled datasets. These datasets are designed to train algorithms into classifying data or predicting outcomes accurately. Using labeled inputs and outputs, the model can measure its accuracy and learn over time. Supervised learning can be separated into two types of problems when data mining: classification and regression. Unsupervised Learning It uses machine learning algorithms to analyze and cluster unlabeled data sets. These algorithms discover hidden patterns in data without the need for human intervention. Unsupervised learning models are used for three main tasks: clustering, association, and dimensionality reduction.

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Slide 13.21: An example of decision tree using Sklearn Slide 14.2: K-nearest neighbor (kNN) algorithm (cont.) Home         Print version

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