Book Image

Data Cleaning and Exploration with Machine Learning

By : Michael Walker
Book Image

Data Cleaning and Exploration with Machine Learning

By: Michael Walker

Overview of this book

Many individuals who know how to run machine learning algorithms do not have a good sense of the statistical assumptions they make and how to match the properties of the data to the algorithm for the best results. As you start with this book, models are carefully chosen to help you grasp the underlying data, including in-feature importance and correlation, and the distribution of features and targets. The first two parts of the book introduce you to techniques for preparing data for ML algorithms, without being bashful about using some ML techniques for data cleaning, including anomaly detection and feature selection. The book then helps you apply that knowledge to a wide variety of ML tasks. You’ll gain an understanding of popular supervised and unsupervised algorithms, how to prepare data for them, and how to evaluate them. Next, you’ll build models and understand the relationships in your data, as well as perform cleaning and exploration tasks with that data. You’ll make quick progress in studying the distribution of variables, identifying anomalies, and examining bivariate relationships, as you focus more on the accuracy of predictions in this book. By the end of this book, you’ll be able to deal with complex data problems using unsupervised ML algorithms like principal component analysis and k-means clustering.
Table of Contents (23 chapters)
1
Section 1 – Data Cleaning and Machine Learning Algorithms
5
Section 2 – Preprocessing, Feature Selection, and Sampling
9
Section 3 – Modeling Continuous Targets with Supervised Learning
13
Section 4 – Modeling Dichotomous and Multiclass Targets with Supervised Learning
19
Section 5 – Clustering and Dimensionality Reduction with Unsupervised Learning

Chapter 5: Feature Selection

Depending on how you began your data analytic work and your own intellectual interests, you might have a different perspective on the topic of feature selection. You might think, yeah, yeah, it is an important topic, but I really want to get to the model building. Or, at the other extreme, you might view feature selection as at the core of model building and believe that you are 90% of the way toward having your model once you have chosen your features. For now, let's just agree that we should spend a good chunk of time understanding the relationships between features – and their relationship to a target if we are building a supervised model – before we do any serious model specification.

It is helpful to approach our feature selection work with the attitude that less is more. If we can reach nearly the same degree of accuracy or explain as much of the variance with fewer features, we should select the simpler model. Sometimes, we...