After a successful application of SVM with linear kernel, we will look at one more example of an SVM with RBF kernel to start with.
We are going to build a classifier that helps obstetricians categorize cardiotocograms (CTGs) into one of the three fetal states (normal, suspect, and pathologic). The cardiotocography dataset we use is from https://archive.ics.uci.edu/ml/datasets/Cardiotocography under the UCI Machine Learning Repository and it can be directly downloaded from https://archive.ics.uci.edu/ml/machine-learning-databases/00193/CTG.xls as an .xls Excel file. The dataset consists of measurements of fetal heart rate and uterine contraction as features, and the fetal state class code (1=normal, 2=suspect, 3=pathologic) as a label. There are in total 2,126 samples with 23 features. Based on the numbers of...
