DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Data Mining Data Mining with Rattle Introduction Data Transform Explore A Model Building Framework Unsupervised Modelling Two Class Models Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Cluster Analysis Conditional Trees Hierarchical Clustering K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

Bayes Classifier:
Classification

Bayes classifiers came in two varieties: naïve and full. Naïve Bayes is a technique for estimating probabilities of individual variable values, given a class, from training data and to then allow the use of these probabilities to classify new entities. Naïve Bayes has been demonstrated usefully on moderate and large datasets. It can be used for diagnosis and classification tasks. Despite the fact that the assumption of conditional independence is often violated the approach continues to work well. Full Bayes ...

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