spam e-mail) and Sentiment Analysis (in social media analysis, to identify positive and negative customer sentiments) • Recommendation System: Naive Bayes Classifier and Collaborative Filtering together builds a Recommendation System that uses machine learning and data mining techniques to filter unseen information and predict whether a user would like a given resource or not PROBLEMS : REFER CLASS NOTES. 2.4 K-nearest neighbor. K-Nearest Neighbors is one of the most basic yet essential classification algorithms in Machine Learning. It belongs to the supervised learning domain and finds intense application in pattern recognition, data mining and intrusion detection. It is widely disposable in real-life scenarios since it is non-parametric, meaning, it does not make any underlying assumptions about the distribution of data (as opposed to other algorithms such as GMM , which assume a Gaussian distribution of the given data).We are given some prior data (also called training data), which classifies coordinates into groups identified by an attribute. The most basic instance-based method is the k-NEARESNTE NEIGHBOR ALGORITHM. This algorithm assumes all instances correspond to points in the n- dimensional space ". The nearest neighbors of an instance are defined in terms of the standard Euclidean distance. More precisely, let an arbitrary instance x be described by the feature vector.
ML(17ISDE651) UNIT 2: REGRESSION AND CLASSIFICATION METHODS SHRUTHISHREE S.H |ASST.PROF|DEPT.OF ISE | JAIN UNIVERSITY Page 13 where For
ML(17ISDE651) UNIT 2: REGRESSION AND CLASSIFICATION METHODS SHRUTHISHREE S.H |ASST.PROF|DEPT.OF ISE | JAIN UNIVERSITY Page 14 • The positive and negative training examples are shown by "+" and "-" respectively. • A query point x, is shown as well. Note the 1- k-NEARESNTE NEIGHBOR algorithm classifies x, as a positive example in this figure, whereas the 5- k- NEARESNTE NEIGHBOR algorithm classifies it as a negative example. •
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