Logistic regression model – building and training

Also based on our explanation of logistic regression in Chapter 2, Data Modeling in Action - The Titanic Example, we are going to implement the logistic regression algorithm in TensorFlow. So, briefly, logistic regression passes the input through the logistic/sigmoid but then treats the result as a probability:

 

Figure 13: Discriminating between two linearly separable classes, 0's and 1's

Utilizing logistic regression in TensorFlow

For us to utilize logistic regression in TensorFlow, we first need to import whatever libraries we are going to use. To do so, you can run this code cell:

import tensorflow as tf

import pandas as pd

import numpy as np
import time
from sklearn.datasets import load_iris
from sklearn.cross_validation import train_test_split
import matplotlib.pyplot as plt

Next, we will load the dataset we are going to use. In this case, we are utilizing the iris dataset, which is inbuilt. So, there's no need to do any preprocessing...