Bayes' Rule b. Identify customers with similar behavior given a large database of customer data containing their propert

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Bayes Rule B Identify Customers With Similar Behavior Given A Large Database Of Customer Data Containing Their Propert 1 (85.85 KiB) Viewed 31 times

Bayes' Rule b. Identify customers with similar behavior given a large database of customer data containing their properties and past buying records, Supervised Learning Application c. Observes inputs and output features then leams to map inputs to output. Unsupervised Learning Application v TF-IDF d. A measure that weight the presence of unusual terms in the query as higher indications of document relevance than the presence of more common terms. N-fold Cross-validation e. The sum of squares of the distances of each data point in all clusters to their respective centroids Regression f. The difference between the base entropy and the conditional entropy of the attribute. Supervised Learning g. Supervised learning with numeric output values h. Discover the structure and similar patterns in the input with no explicit feedback Unsupervised Learning Decision Tree Randomly splitting the dataset into N non-overlapping subsets and then fitting a model using N-1 groups and predicting its performance using the group that was held out. vClassification J. Pab) = [P(ba) P(a) ]/P(b) Pure nodes k. P(ab) = [P(ba)*P(b) ]/P(b) Within Sum of Squares (WSS) 1. It provides a measure that will weight the presence of common terms in the query as higher indications of document relevance than the presence more unusual terms m. Intemal nodes test a value of an attribute, leaf nodes-class labels. n. Determine whether a credit card transaction is valid or fraudulent. o. Supervised learning with a discrete set of possible output values. p. The sum of squared distances of multiple means to the global mean.