Principal components clustering methods k means

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patterns in data (“data mining”) that may not be evident initially. Principal Components Clustering Methods K Means Hierarchical Bayesian
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K-Nearest Neighbor K-Nearest Neighbor: “The KNN Classifier” – a nonparametric algorithm that can be used for classification and regression.
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K-Nearest Neighbor Parametric vs. Non-parametric: Parametric: Non-parametric:
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K-Nearest Neighbor Parametric vs. Non-parametric: Parametric: Assumes a specific distribution for variable(s). Non-parametric: Does not make any assumptions with regard to the distribution of variable(s).
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K-Nearest Neighbor K-Nearest Neighbor: “The KNN Classifier” – a nonparametric algorithm that can be used for classification and regression. Objective: To classify a binary random variable in a test set as 0 or 1 based comparisons to the training data.
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K-Nearest Neighbor K-Nearest Neighbor: “The KNN Classifier” – a nonparametric algorithm that can be used for classification and regression. Objective: To classify a binary random variable in a test set as 0 or 1 based comparisons to the training data. How: By calculating the distance between k nearest training observations for a given test observation, and assigning that observation as either 0 or 1 based on a vote of the corresponding Y (0 or 1) values of the k nearest observations.
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K-Nearest Neighbor K-Nearest Neighbor: “The KNN Classifier” Logic of the KNN Algorithm: KNN is based on feature similarity – i.e., how closely features resemble our training set determines how we classify a given data point.
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K-Nearest Neighbor K-Nearest Neighbor: “The KNN Classifier” Logic of the KNN Algorithm: KNN is based on feature similarity – i.e., how closely features resemble our training set determines how we classify a given data point. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors .
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K-Nearest Neighbor K-Nearest Neighbor: “The KNN Classifier” Logic of the KNN Algorithm: KNN is based on feature similarity – i.e., how closely
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