Logistic regression classifier

This approach can be chosen where the output can take only two values, 0 or 1, pass/fail, win/lose, alive/dead, or healthy/sick, and so on. In cases where the dependent variable has more than two outcome categories, it may be analyzed using multinomial logistic regression.

How to do it...

1. After installing the essential packages, let's construct some training labels:

import numpy as np
from sklearn import linear_model
import matplotlib.pyplot as plt
a = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
b = np.array([1, 1, 1, 2, 2, 2])

2. Initiate the classifier:

classification = linear_model.LogisticRegression(solver='liblinear', C=100)
classification.fit(a, b)

3. Sketch datapoints and margins:

def plot_classification(classification, a , b):
  a_min, a_max = min(a[:, 0]) - 1.0, max(a[:, 0]) + 1.0
  b_min, b_max = min(a[:, 1]) - 1.0, max(a[:, 1]) + 1.0 step_size = 0.01
  a_values, b_values = np.meshgrid(np.arange(a_min, a_max, step_size), np.arange(b_min, b_max...