R for Classification Jennifer Broughton Shimadzu Research Laboratory Manchester, UK 2 nd May 2013

Classification? Automatic Identification of Type (Class) of Object from Measured Variables (Features) Object TypeFeature1Feature2Feature3…….Feature n Label 1val[1,1]val[1,2]val[1,3]……. val[1,n] Label 2val[2,1]val[2,2]val[2,3]…….val[2,n] ………….…….…….…….……… Label mval[m,1]val[m.2]val[m,3]…….val[m,n] 2 of 17

Example Data 3 of 17

Data Preparation & Investigation EDA Technique Box Plots PCA Decision Trees Clustering Training Set Best features to distinguish between classes Relationships between features Feature reduction 4 of 17

Box Plots PCA & Multivariate Analysis: ade4 FactoMineR 5 of 17

Example Classifier 6 of 17

Classification Algorithms in R Rattle: R Analytical Tool to Learn Easily ( Rattle: A Data Mining GUI for R, Graham J Williams, The R Journal, 1(2):45-55 ) 7 of 17

SVM 8 of 17

Ensemble Algorithm 9 of 17

Training and Testing Classification Algorithm: Neural Network Support Vector Machine Random Forest Training Set (labelled) Test Set (unlabelled) Trained Classifier Classification Results Prediction Results + Labels Assess Predictions: Confusion Matrix ROC Curve (2 categories) …. 10 of 17

Using Classifiers in R Select Training Data Build Classifier Run Classifier classifier  algorithm(formula, data, options) (boosting and nnet) classifier.pred  predict(classifier, newdata, options) 11 of 17

SVM & Neural Net Tuning 12 of 17

Classifier Feedback print(classifier) plot(classifier) high Gini Coefficient = high dispersion 13 of 17

Classifier Prediction Results predict(type = “class”) predict(type = “prob”) confusion matrix 14 of 17

False Negative False Negative True Positive True Positive True Negative True Negative False Positive False Positive Binary Classification Results Y N Class Present? Class Detected? Y N   15 of 17

ROC Curves in R ROCR package 16 of 17

Example Results 17 of 17