Machine Learning with Python Cookbook

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, x