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

Key concepts for SVC

We can use support vector machines (SVMs) to find a line or curve to separate instances by class. When classes can be discriminated by a line, they are said to be linearly separable.

There may, however, be many possible linear classifiers, as we can see in Figure 13.1. Each line successfully discriminates between the two classes, represented by dots and squares, using the two features x1 and x2. The key difference is in how the lines would classify new instances, represented by the transparent rectangle. Using the line closest to the squares would cause the transparent rectanglez to be classified as a dot. Using either of the other two lines would classify it as a square.

Figure 13.1 – Three possible linear classifiers

When a linear discriminant is very close to training instances, as is the case with two of the lines in Figure 13.2, there is a greater risk of misclassifying new instances. We want a line that gives us the maximum...