Machine Learning with Python Cookbook, 2nd Edition

Chapter 16. Logistic Regression

16.0 Introduction

Despite being called a regression, logistic regression is actually a widely used supervised classification technique. Logistic regression (and its extensions, like multinomial logistic regression) is a straightforward, well-understood approach to predicting the probability that an observation is of a certain class. In this chapter, we will cover training a variety of classifiers using logistic regression in scikit-learn.

16.1 Training a Binary Classifier

Problem

You need to train a simple classifier model.

Solution

Train a logistic regression in scikit-learn using LogisticRegression:

# Load libraries
from sklearn.linear_model import LogisticRegression
from sklearn import datasets
from sklearn.preprocessing import StandardScaler

# Load data with only two classes
iris = datasets.load_iris()
features = iris.data[:100,:]
target = iris.target[:100]

# Standardize features
scaler = StandardScaler()
features_standardized = scaler.fit_transform(features)

# Create logistic regression object
logistic_regression = LogisticRegression(random_state=0)

# Train model
model = logistic_regression.fit(features_standardized, target)

Discussion

Despite having “regression” in its name, a logistic regression is actually a widely used binary classifier (i.e., the target vector can take only two values). In a logistic regression, a linear model (e.g., β₀ + β₁x) is included in a logistic (also called sigmoid) function, $\frac{1}{1+e^{-z}}$, such that:

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