Building a logistic regression classifier

Despite the word regression being present in the name, logistic regression is actually used for classification purposes. Given a set of datapoints, our goal is to build a model that can draw linear boundaries between our classes. It extracts these boundaries by solving a set of equations derived from the training data.

How to do it…

1. Let's see how to do this in Python. We will use the logistic_regression.py file that is provided to you as a reference. Assuming that you imported the necessary packages, let's create some sample data along with training labels:

   import numpy as np
   from sklearn import linear_model 
   import matplotlib.pyplot as plt
   
   X = np.array([[4, 7], [3.5, 8], [3.1, 6.2], [0.5, 1], [1, 2], [1.2, 1.9], [6, 2], [5.7, 1.5], [5.4, 2.2]])
   y = np.array([0, 0, 0, 1, 1, 1, 2, 2, 2])

   Here, we assume that we have three classes.

2. Let's initialize the logistic regression classifier:

   classifier = linear_model.LogisticRegression(solver='liblinear', C=100...