Passion is like genius; a miracle.

knn, k-nearest neighbor, algorithm is a non parametric classification using neighbor of data to test. The idea is simple, and we can simply use knn() in ‘class’ package. But one thing to remember is that we should normalize data before measuring distance. For example, suppose that we measured variable x in cm and y in…

Chi-square test can be used for testing independence using the formula at wiki. Given a table: We want to know the values are independent from class, i.e., A, B, C. It’s very trivial in R: Or one can use matrix: Or one can just list of values or its table: Two things to remember: –…

http://alex.smola.org/teaching/berkeley2012/index.html Scalable machine learning class at berkeley with vids. at youtube and pdf slides. Nice resource for studying big data mining. I really appreciate researchers who make their teaching materials freely available.

Local Outlier Factor(LOF) is a state-of-the-art for finding outlier (according to the book Data Mining with R). Its main idea is to finding objects whose local density is considerably lower than local density of its neighbor. DMwR has an implementation lofactor() to compute LOF.

Package e1071 provides with naiveBayes function. It assumes independence of predictors, and assumes Gaussian distribution for metric predictors. Its example includes iris sample: Our target variable is Species: To see its performance (remember that 5th column is Species): For prediction, use predict. When type=”raw” is given, probability is printed:

rdatamining.com has a nice documents: http://www.rdatamining.com/docs. Esp., don’t miss R reference cards containing the list of important data mining functions for r.

Package DMwR which is heavily described by the book Data Mining with R: Learning with Case Studies has several interesting libraries. Among them, SMOTE is easy to use function to handle class imbalance. To quote the example of the package, first, generate small sample example: Then, we generate new data set by 1) adding new…

http://www2.cs.uregina.ca/~dbd/cs831/notes/lift_chart/lift_chart.html http://www.nd.edu/~busiforc/handouts/DataMining/Lift%20Charts.html Cumulative gains and lift charts compares the predictive model with baseline. It emphasizes recall than precision.