Python: Real World Machine Learning

Extracting confidence measurements

It would be nice to know the confidence with which we classify unknown data. When a new datapoint is classified into a known category, we can train the SVM to compute the confidence level of this output as well.

How to do it…

1. The full code is given in the svm_confidence.py file already provided to you. We will only discuss the core of the recipe here. Let's define some input data:

   # Measure distance from the boundary
   input_datapoints = np.array([[2, 1.5], [8, 9], [4.8, 5.2], [4, 4], [2.5, 7], [7.6, 2], [5.4, 5.9]])

2. Let's measure the distance from the boundary:

   print "\nDistance from the boundary:"
   for i in input_datapoints:
       print i, '-->', classifier.decision_function(i)[0]

3. You will see the following printed on your ...