After a successful application of SVM with the linear kernel, we will look at one more example where SVM with the RBF kernel is suitable for it.
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 via 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 fetal state class code (1=normal, 2=suspect, 3=pathologic) as label. There are, in total, 2126 samples with 23 features. Based on the numbers of instances and features ...
