A further example – breast cancer classification using SVM with TensorFlow

So far, we have been using scikit-learn to implement SVMs. Let's now look at how to do so with TensorFlow. Note that, up until now (the end of 2018), the only SVM API provided in TensorFlow is with linear kernel for binary classification.

We are using the breast cancer dataset (https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)) as an example. Its feature space is 30-dimensional, and its target variable is binary. Let's see how it's done by performing the following steps:

1. First, import the requisite modules and load the dataset as well as check its class distribution:

>>> import tensorflow as tf
>>> from sklearn import datasets
>>> cancer_data = datasets.load_breast_cancer()
>>> X = cancer_data.data
>>> Y = cancer_data...