Machine Learning with Python Cookbook, 2nd Edition

Chapter 4. Handling Numerical Data

4.0 Introduction

Quantitative data is the measurement of something—​whether class size, monthly sales, or student scores. The natural way to represent these quantities is numerically (e.g., 29 students, $529,392 in sales). In this chapter, we will cover numerous strategies for transforming raw numerical data into features purpose-built for machine learning algorithms.

4.1 Rescaling a Feature

Problem

You need to rescale the values of a numerical feature to be between two values.

Solution

Use scikit-learn’s MinMaxScaler to rescale a feature array:

# Load libraries
import numpy as np
from sklearn import preprocessing

# Create feature
feature = np.array([[-500.5],
                    [-100.1],
                    [0],
                    [100.1],
                    [900.9]])

# Create scaler
minmax_scale = preprocessing.MinMaxScaler(feature_range=(0, 1))

# Scale feature
scaled_feature = minmax_scale.fit_transform(feature)

# Show feature
scaled_feature

array([[ 0.        ],
       [ 0.28571429],
       [ 0.35714286],
       [ 0.42857143],
       [ 1.        ]])

Discussion

Rescaling is a common preprocessing task in machine learning. Many of the algorithms described later in this book will assume all features are on the same scale, typically 0 to 1 or –1 to 1. There are a number of rescaling techniques, but one of the simplest is called min-max scaling. Min-max scaling uses the minimum and maximum values of a feature to rescale values to within a range. Specifically, min-max calculates:

$$x_i^{\text{'}}=\frac{x_i-\text{min}\left(x\right)}{\text{max}\left(x\right)-\text{min}\left(x\right)}$$

where $x$ is the feature vector,