This is a course project (hw-560-project two)

Background/requirement

• Project Type: End to End Data Mining Project

• Project Goal

  • Use different classifiers to create several classification models
  • Compare the predictive performance for each model.
  • Combining the output from the models in an ensemble fashion

• Deliverables

  • Documented R Markdown Output
  • Github repository
  • Canvas submission

Dataset

BreastCancer {mlbench} Wisconsin Breast Cancer Database

Description The objective is to identify each of a number of benign or malignant classes. Samples arrive periodically as Dr. Wolberg reports his clinical cases. The database therefore reflects this chronological grouping of the data. This grouping information appears immediately below, having been removed from the data itself. Each variable except for the first was converted into 11 primitive numerical attributes with values ranging from 0 through 10. There are 16 missing attribute values. See cited below for more details.

Format A data frame with 699 observations on 11 variables, one being a character variable, 9 being ordered or nominal, and 1 target class.

[,1] Id Sample code number [,2] Cl.thickness Clump Thickness [,3] Cell.size Uniformity of Cell Size [,4] Cell.shape Uniformity of Cell Shape [,5] Marg.adhesion Marginal Adhesion [,6] Epith.c.size Single Epithelial Cell Size [,7] Bare.nuclei Bare Nuclei [,8] Bl.cromatin Bland Chromatin [,9] Normal.nucleoli Normal Nucleoli [,10] Mitoses Mitoses [,11] Class Class

Process

• Load required packages
• Data preparation
  • the dataset is cleaned
• EDA - not required
• Modeling
  • logistic regression - not required
  • knn() - not required
  • naive bayes : e1071::NaiveBayes(), klaR::NaiveBayes()
  • classification tree: caret::rpart()
    • with cross-validation (LOOCV)
  • conditional reference tree: party::ctree()
  • random forest: randomForest::randomForest(), party::cforest()
  • bagging (bootstrap aggregating): ipred::bagging()
  • svm: e1071::svm()
  • neutral network: nnet:nnet()
  • Quadratic Discriminant Analysis: MASS::qda()
  • Regularised Discriminant Analysis: klaR::rda()
• Plot the ROC curves for each model
• Ensemble